Market Efficiency, Behavior and Information Asymmetry ...

TECHNISCHE UNIVERSITAumlT DARMSTADT

DISSERTATION

Market Efficiency Behavior andInformation Asymmetry Empirical

Evidence from Cryptocurrency and StockMarkets

Vorgelegt vonDavid HAumlFNER MScgeboren am 07121990in Schwaumlbisch HallDeutschland

ErstgutachterProf Dr Dirk SCHIERECK

ZweitgutachterProf Dr Peter BUXMANN

Vom Fachbereich Rechts- und Wirtschaftswissenschaften derTechnischen Universitaumlt Darmstadt

zur Erlangung des akademischen GradesDoctor rerum politicarum (Dr rer pol)

Darmstadt 2020

i

Haumlfner David Market Efficiency Behavior and Information Asymmetry EmpiricalEvidence from Cryptocurrency and Stock MarketsDarmstadt Technische Universitaumlt DarmstadtTag der Einreichung 02072020Tag der muumlndlichen Pruumlfung 15122020URN urnnbndetuda-tuprints-174953Jahr der Veroumlffentlichung der Dissertation auf TUprints 2021Veroumlffentlicht unter CC BY-NC-ND 40 Internationalhttpscreativecommonsorglicenses

ii

ldquoI donrsquot know what I may seem to the world but as to myself I seem to have been only likea boy playing on the sea shore and diverting myself in now and then finding a smootherpebble or a prettier shell than ordinary whilst the great ocean of truth lay all undiscoveredbefore merdquo

Sir Isaac Newton

iii


AbstractFachbereich Rechts- und Wirtschaftswissenschaften

Fachgebiet Unternehmensfinanzierung

Dr rer pol

Market Efficiency Behavior and Information Asymmetry Empirical Evidencefrom Cryptocurrency and Stock Markets

by David HAumlFNER

This dissertation is dedicated to the analysis of three superordinate economic prin-ciples in varying market environments market efficiency the behavior of marketparticipants and information asymmetrySustainability and social responsibility have gained importance as investment crite-ria in recent years However responsible investing can lead to conflicting goals withrespect to utility-maximizing behavior and portfolio diversification in efficient mar-kets Conducting a meta-analysis this thesis presents evidence that positive (non-monetary) side effects of responsible investing can overcome this burden Next theimpact of the EU-wide regulation of investment research on the interplay betweeninformation asymmetry idiosyncratic risk liquidity and the role of financial analystsin stock markets is investigated An empirical analysis of the emerging primary andsecondary market for cryptocurrencies yields further insights about the effects ofinformation asymmetry between investors issuers and traders The efficient alloca-tion of resources is dependent on the market microstructure the behavior of marketparticipants as well as exogenous shocks Against this background this thesis isdedicated to the empirical analysis of limit order books the rationality of tradersand the impact of COVID-19 Due to its young history the market for cryptocurren-cies yields a suitable research subject to test classical financial theories This doctoralthesis reveals parallels between the microstructure of cryptocurrency and stock mar-kets and uncovers some previously unknown statistical properties of the cryptocur-rency market microstructure An initial examination of the impact of COVID-19further shows that cryptocurrencies with a high market capitalization seem to reactto macroeconomic shocks similar to stock marketsThis cumulative dissertation comprises six stand-alone papers of which three pa-pers have already been published

iv

ZUSAMMENFASSUNG (DEUTSCHE VERSION)

Diese Dissertation widmet sich der Analyse von drei uumlbergeordneten wirtschafts-wissenschaftlichen Konzepten in verschiedenen Marktumfeldern MarkteffizienzVerhalten von Marktakteuren und InformationsasymmetrieIn den letzten Jahren haben Nachhaltigkeit und soziale Verantwortung als entschei-dungsrelevante Investitionskriterien staumlndig an Bedeutung gewonnen Verantwor-tungsvolles Verhalten kann auf effizienten Maumlrkten allerdings zu einem Zielkon-flikt hinsichtlich der individuellen Nutzenmaximierung und der Portfoliodiversifi-kation fuumlhren Diese Arbeit liefert anhand einer Metaanalyse Evidenz dafuumlr dass(nicht-monetaumlre) Nebeneffekte verantwortungsvollen Investierens diesen Zielkon-flikt uumlberwinden koumlnnen Anschlieszligend werden die Auswirkungen der EU-weitenRegulierung von Investment Research auf das Zusammenspiel von Informationsa-symmetrie idiosynkratischem Risiko Liquiditaumlt und die Rolle von Finanzanalys-ten im Aktienmarkt untersucht Daruumlber hinaus liefert eine empirische Analysedes aufstrebenden Primaumlr- und Sekundaumlrmarkts fuumlr Kryptowaumlhrungen neue Er-kenntnisse uumlber die Auswirkungen von Informationsasymmetrien zwischen Inves-toren Emittenten und Haumlndlern Die effiziente Allokation von Ressourcen haumlngtvon der Marktmikrostruktur dem Verhalten von Marktakteuren sowie von exoge-nen Schocks ab Vor diesem Hintergrund widmet sich diese Arbeit der empirischenAnalyse von Limit-Orderbuumlchern der Rationalitaumlt von Haumlndlern und den Auswir-kungen von COVID-19 Der Markt fuumlr Kryptowaumlhrungen bietet aufgrund seinerjungen Historie einen geeigneten Forschungsgegenstand um klassische Finanzie-rungstheorien empirisch zu testen Diese Dissertation zeigt Parallelen zwischen derMikrostruktur von Kryptowaumlhrungs- und Aktienmaumlrkten auf und deckt einige bis-her unbekannte statistische Eigenschaften der Marktmikrostruktur von Kryptowaumlh-rungen auf Eine erste Untersuchung der Auswirkungen von COVID-19 zeigt zu-dem dass insbesondere Kryptowaumlhrungen mit einer hohen Marktkapitalisierungaumlhnlich wie Aktienmaumlrkte auf makrooumlkonomische Schocks zu reagieren scheinenDiese kumulative Dissertation umfasst sechs eigenstaumlndige Artikel von denen dreiArtikel bereits veroumlffentlicht wurden

v

AcknowledgementsI want to thank Regina for her unconditional support and understanding during thepreparation of this dissertation I want to thank Prof Dr Schiereck for the veryfruitful discussions and his valuable input during my time at his chair

vi

Contents

Abstract iii

Acknowledgements v

1 Synopsis 111 Motivation 112 Thesis Structure 3

2 What do we know about socially responsible investments 8

3 The Role of Investment Research in view of MiFID II An Empirical Ana-lysis of Information Asymmetry Idiosyncratic Risk and Liquidity 931 Introduction 932 Regulation of Investment Research 11

321 Investment Research in the European Union 11322 Investment Research in the United States 13

33 The Role of Investment Research in Capital Markets 1434 Variable Definition 17

341 Illiquidity 18342 The Bid-Ask Spread 19343 Stock Price Informativeness and Idiosyncratic Risk 21

35 Data 22351 Descriptive Statistics 24

36 Empirical Analysis 28361 Difference-in-Differences Approach 29362 Multiple Regression on the Bid-Ask Spread 30363 Multiple Regression on Idiosyncratic Risk 32364 Summary Statistics on Medium-Term Effects on Research Cov-

erage 3337 Conclusion 34

4 Innovative Finanzierungen uumlber Initial Coin Offerings Struktur und bis-herige Performance 41

vii

5 Statistical Properties of Cryptocurrency Order Books 4251 Introduction 4252 Structure of a Limit Order Book 4353 Data Collection and Description 46

531 Data Acquisition Process 50532 Descriptive Statistics 51

54 Limit Order Book Characteristics in Cryptocurrency Markets 53541 The Shape of the Average Order Book 53542 The Bitcoin Order Book 57543 The Bitcoin Cash Order Book 62544 The Ethereum Order Book 66545 The Lazy Investor Hypothesis 68546 Convexity of the Order Book Slope 71

55 Informativeness of Order Book Characteristics 72551 Measuring the Slope of the Limit Order Book 74552 Order Book Slope and Volatility 75553 Order Book Slope and Trading Activity 77554 Order Book Slope and the Volume-Volatility Relation 78555 A Note on Causality 80

56 Conclusion 84

6 Heuristics in Cryptocurrency Limit Order Placement 8561 Introduction 8562 Statistics and Distribution of Incoming Limit Order Prices 8663 Limit Order Placement 8764 Empirical Results 8965 Conclusion 91

7 Reaktionen der Kryptowaumlhrungsmaumlrkte auf die COVID-19-Pandemie 95

8 Concluding Remarks 96

Bibliography 102

viii

List of Figures

31 Research Coverage dependent on Market Capitalization 2532 Empirical Distribution of Research Coverage per Firm 2533 Sample Distribution of Stock Spreads 2634 Analyst Coverage and the Relative Bid-Ask spreadthe Amihud (2002)

Illiquidity Measure 2735 Empirical Distribution of the Idiosyncratic Risk Measure Ψ 28

51 Structure of a Limit Order Book 4552 Waterfall Model for Tokenized Real Assets 4953 Ask Volume and Aggregated Ask Volume of the BTCUSD Limit Or-

der Book relative to the Distance (∆) towards the best Ask Price 5854 Bid Volume and Aggregated Bid Volume of the BTCUSD Limit Or-

der Book relative to the Distance (∆) towards the best Bid Price 5955 Average Ask and Bid Volume of the BTCUSD Limit Order Book rel-

ative to the Distance Delta (∆) towards the Best Bid or Ask Price 6056 Ask Volume and Aggregated Ask Volume of the BCHUSD Limit Or-

der Book relative to the Distance (∆) towards the best Ask Price 6357 Bid Volume and Aggregated Bid Volume of the BCHUSD Limit Or-

der Book relative to the Distance (∆) towards the best Bid Price 6358 Average Ask and Bid Volume of the BCHUSD Limit Order Book rel-

ative to the Distance Delta (∆) towards the Best Bid or Ask Price 6459 Ask Volume and Aggregated Ask Volume of the ETHUSD Limit Or-

der Book relative to the Distance (∆) towards the best Ask Price 66510 Bid Volume and Aggregated Bid Volume of the ETHUSD Limit Or-

der Book relative to the Distance (∆) towards the best Bid Price 67511 Average Ask and Bid Volume of the ETHUSD Limit Order Book rel-

ative to the Distance Delta (∆) towards the Best Bid or Ask Price 67512 ConvexityConcavity of the Aggregated BTCUSD Limit Order Book 71

61 Incoming Limit Orders and Fitted Power-Law 9362 Fitted Power-Law and Extended Model for BTCUSD 94

ix

List of Tables

11 Research Setting and Data 612 Research Focus across Chapters 7

31 Summary Statistics by Country 3632 Summary Statistics for Analyst Coverage 3733 The Effect of MiFID II on European Financial Markets 3734 The Impact of Analyst Coverage on Information Asymmetry 3835 The Impact of Analyst Coverage on Idiosyncratic Risk 3936 Medium-Term Development of Analyst Coverage since the Introduc-

tion of MiFID II (2018 ndash 2019) 40

51 Example of events occuring in a Cryptocurrency Order Book 5152 Sample Overview and Meta Statistics of Trading Data 5453 Breakdown of Order Types and Cancellation Rates 5554 Conditional Probabilities of the Order Type in Time t given the Order

Type in tminus 1 5655 Slope of the Aggregated Order Book for BTCUSD 6156 Slope of the Aggregated Order Book for BCHUSD 6557 Slope of the Aggregated Order Book for ETHUSD 6958 Volume peaks in Cryptocurrency Limit Order Books 7059 ConcavityConvexity of Aggregated Cryptocurrency Limit Order Books 73510 Volatility and the Slope of the Order Book 77511 Trading Activity and the Slope of the Order Book 79512 The Volume-Volatility relation and the Slope of the Order Book 81513 Linear Granger Causality Test Results on Trades 82514 Linear Granger Causality Test Results on Volatility 83

61 Descriptive Statistics 8862 Fitted Power Law Results 90

x

List of Abbreviations

ADJR Adjustierte RenditeAMF Autoriteacute des Marcheacutes FinanciersAPI Application Programming InterfaceBaFin Bundesanstalt fuumlr FinanzdienstleistungsaufsichtBCH Bitcoin CashBTC BitcoinCAPM Capital Asset Pricing ModelCEO Chief Executive OfficerCOVID-19 Coronavirus Disease 2019CSR Corporate Social ResponsibilityEBA Europaumlische BankenaufsichtECN Electronic Communications NetworkEMH Efficient Market HypothesisEPS Earnings per ShareERC Ethereum Request for CommentsESG Environmental Social and GovernanceESMA European Securities and Markets AuthorityETF Exchange-Traded FundETH EthereumEU European UnionEUR EuroHML High Minus LowHFT High Frequency TradingIBES Institutional Brokersrsquo Estimate SystemICO Initial Coin OfferingIPO Initial Public OfferingISIN International Securities Identification NumberKMU Kleine und mittlere UnternehmenLOB Limit Order BookLTC LitecoinMampA Mergers and AcquisitionsMiFID Markets in Financial Instruments DirectiveMOM MomentumNYSE New York Stock Exchange

xi

OLS Ordinary Least SquaresRampD Research and DevelopmentRER Rohe EmissionsrenditeSEC United States Securities and Exchange CommissionSIC Standard Industrial Classification CodeSMB Small Minus BigSRI Socially Responsible InvestmentUS United States of AmericaUSD US DollarVC Venture Capital

xii

Dedicated to my parents

1

Chapter 1

Synopsis

11 Motivation

Financial markets contain an incredible amount of information about the human be-havior in a competitive setting Market participants express their believes about thevalue of an asset by indicating the willingness of selling or buying the asset at a spe-cific price Loosely speaking market participants put money where their mouth iswhen trading an asset and are monetary incentivized to act rational This design offinancial markets yields a fertile environment to empirically test economic financialand behavioral hypothesesFinancial markets shape the world we live in and play a crucial role in distributingresources efficiently and by uncovering hidden market characteristics and flaws inmarket design science can add to a broader understanding of financial markets andthereby benefit the societyOne of the most important discoveries in the finance literature is the concept of mar-ket efficiency Market efficiency guarantees that the best available projects receivefunding Fama (1970) hypothesizes that markets are only efficient if all available in-formation is reflected in asset prices and no market participant can make long termprofits However Fama (1970) differentiates between different levels of market effi-ciency and it remains uncertain to this day to what extent financial markets are effi-cient Furthermore ndash accompanied by the technological advancement of humankindndash markets are in constant change and new markets arise while others collapse How-ever there seem to exist universal characteristics and repetitive patterns hidden inthe nature of financial markets and the competitive behavior of market participantsobservable across different assets eras and market environmentsSince establishing the efficient market hypothesis critics on the validity emergedand empirical data suggests that anomalies exist in financial markets and imperfec-tions are often better explained by cognitive biases and irrational behavioral of mar-ket participants Moreover social responsibility and regulatory constraints shapethe market environment and impact the behavior of market participantsWhile traditional financial theories focus on the wealth of one individual and viewthe maximization of the present value or a typically unobservable individual utility

Chapter 1 Synopsis 2

function as the sole dictum more recent models also account for investors who mayexhibit other strategic patterns depending on their individual behaviorMoreover conflicts of aims between stakeholders and the general welfare requireconcepts like social and environmental responsibility to be taken into account dur-ing investment decisions Despite its importance the compatibility of social respon-sibility and rational investment decisions is academically not conclusively clarifiedand understood making further research necessaryThis dissertation aims to explore the behavior and interaction of agents in financialmarkets in different market conditions study the impact of real world features oncapital allocation and is driven by examining the facets of three superordinate con-cepts relevant for financial markets

bull Market Efficiency

bull Behavior of Market Participants

bull Information Asymmetry

This thesis aims to provide new findings that contribute to society and especiallyacademia and is geared towards broadening the understanding of interactions infinancial markets the impact of social and regulatory constraints the behavior ofmarket participants and the effect of information disparity across agents Derivedfrom the concepts of market efficiency the behavior of market participants and infor-mation asymmetry we formulate several research questions that act as a guidelinethroughout this dissertation and motivate the following chapters

bull Research Question 1 Is the concept of market efficiency compatible with so-cial responsibility

bull Research Question 2 How do participants in highly efficient stock marketsreact to an exogenous shock in market design (regulatory change)

bull Research Question 3 How do investors cope with information asymmetry

bull Research Question 4 How does the market microstructure shape market dy-namics

bull Research Question 5 How do traders behave when placing limit buy and sellorders in a competitive market setting

bull Research Question 6 How does the cryptocurrency market react to an exoge-nous shock (market crisis)


12 Thesis Structure

This thesis is structured into six stand-alone research papers The thesis comprisesthree published papers (Chapter 2 Chapter 4 and Chapter 7) and three unpublishedresearch papers (Chapter 3 Chapter 5 and Chapter 6)In Chapter 2 What do we know about socially responsible investments we conduct aliterature review on socially responsible investments (SRIs) from three different per-spectives The perspective of an investor engaging in SRIs the company and man-agement that acts socially responsible and the performance of institutional fundsengaging in SRIs We discuss corporate social responsibility (CSR) and the three cen-tral factors environmental social and governance (ESG) as major decision drivers ina firmrsquos responsible management We further analyze investors who demand socialresponsibility from firms and institutional funds with an SRI focus which are oftenrestricted to only invest in companies that fulfill various SRI screening criteria Incor-porating certain social constraints in the investment strategy reduces the size of thepossible investment universe which should ceteris paribus not lead to a superior fi-nancial performance However our meta study shows that financial performance isnot necessarily the only objective of investors and company management Investingand acting socially and environmentally responsible can have non-monetary bene-fits Thus while financial gain and social responsibility are oftentimes conflictinggoals the positive side effects of responsible investing and management can over-come this burden We also highlight the motivation the behavior and demographicsof socially responsible investors and discuss the (non-)financial motivation of man-agers to act responsible and outline characteristics of socially responsible companiesFinally we discuss portfolio implications for investment funds under regional con-siderations risk and uncertainty and financial performance when engaging in SRIs

In Chapter 3 The Role of Investment Research in view of MiFID II An EmpiricalAnalysis of Information Asymmetry Idiosyncratic Risk and Liquidity we focus on theimpact of regulation on key characteristics of financial markets Regulation aimsto improve market quality by increasing price stability transparency and therebysecuring capital supply for the economy However implementing new market reg-ulation is inevitably accompanied by direct and indirect costs that are often borneby market participants and regulators have to carefully weigh up the pros and consof regulation Therefore understanding the consequences of market interventions isimportant for a broad range of stakeholders including regulators listed companiesinvestors and economists alike Based on this theoretical background we study theimpact of the revised EU Markets in Financial Instruments Directive (MiFID II) onEuropean financial markets MiFID II came into force in January 2018 targeting theregulation of investment research services across all EU member states Investment


research provided by financial analysts plays a crucial role in information dissem-ination about company specific topics Meanwhile financial literature still strug-gles to explain the exact role of financial analysts While they should not be able togather valuable information in efficient markets they seem to play a crucial role bydistributing information between investors and companies via investment researchservices and reports We aim to contribute to the literature by analyzing the roleof financial analysts during a regulatory change (MiFID II) that heavily affects theway investment research is conducted Using Data about 1646 US firms and 1281EU firms we compare the US and EU approach to regulate investment research ser-vices We find a decline in stock liquidity and an increase in idiosyncratic risk forstocks affected by MiFID II In a second step we focus on the informational role ofresearch analysts and hypothesize that MiFID II increased the competition betweenfinancial analysts leading to higher quality work and consequently to a higher in-formativeness of stock prices While we do not find that analyst coverage decreasedin the short term we find that the amount of coverage in EU markets declined inthe medium term We further find a significant decrease in stock liquidity and anincrease in the bid-ask spread idiosyncratic risk and the level of asymmetric infor-mation that can be associated with MiFID II Finally we find empirical evidencesuggesting that MiFID II affects the informational role research coverage has on thebid-ask spread and a stockrsquos idiosyncratic riskIn Chapter 4 Innovative Finanzierung uumlber Initial Coin Offerings Struktur und bis-herige Performance we study how transparency issues and information asymmetrybetween different parties affects the capital allocation Initial Coin Offerings (ICOs)are a relatively new way of raising capital and due to their simplicity low regulatoryburdens and marginal direct costs they are typically carried out by entrepreneurialand smaller companies We show that ICOs share many similarities with traditionalInitial Public Offerings (IPOs) A much researched phenomenon observed duringthe IPO process is the underpricing effect which is defined as a positive stock re-turn at the first trading day of a stock Academia provides different theories thattry to explain this persistent phenomenon (eg Ritter 1987) While behavioral ap-proaches exist as well popular theories focus on information asymmetries betweenthe issuing company the underwriter and investors In these models underpricingcompensates uninformed investor or signals company well-being We discuss thedifferent theories in detail and empirically show that underpricing can be observedduring ICOs as well and is even more pronounced During ICOs companies typi-cally do not provide much information about the aspired use of the capital neededcreating an environment of high information asymmetry between investors and theissuing company Hence the empirically observed level of underpricing could be aresult of investors demand for a payoff compensating uncertainty We find that com-panies generally leave money on the table during an ICO and could increase theircapital raised by optimizing their communication of information towards investorsduring an ICO


In Chapter 5 Statistical Properties of Cryptocurrency Order Books we focus on themarket microstructure of a new market segment Motivated by the results of theprevious chapter which targets information asymmetries in the primary market forcryptocurrencies we now focus our analysis on the secondary market by analyzingthe market microstructure in cryptocurrency markets In a first step we analyzetraders activities expressed in the limit order book (LOB) and compute the aggre-gated LOB volume ie the slope of the order book dependent on the price levelfor three major cryptocurrencies Bitcoin Bitcoin Cash and Ethereum We find vol-ume peaks at certain relative price levels distant to the best price We show that thesevolume peaks are statistically significant and can be found across all observed LOBsNext we test if there is information hidden in the LOB by analyzing the relation ofthe daily average order book slope and daily price changes trading volume and thevolume-volatility relation found in stock markets We find significant links betweenthe slope of the LOB and returns indicating that investors consider the whole LOBwhen buying or selling cryptocurrencies We further find links between trading ac-tivity and the slope of the LOB but the relation switches sign when considering thefull depth of the LOB While its existence is quite puzzling this market anomaly hasbeen documented in stock markets as well by Naeligs and Skjeltorp (2006) We furtherfind that trading volume and volatility are positively correlated indicating that thevolume-volatility relation which has been documented in the literature across dif-ferent stock markets exists in cryptocurrency markets as well and therefore seemsto be a market independent characteristicIn Chapter 6 Heuristics in Cryptocurrency Limit Order Placement we focus on thebehavior of cryptocurrency investors as these market participants directly impactthe market movements with their buying and selling behavior Using data aboutincoming limit order prices we empirically show that cryptocurrency traders ap-ply heuristics during capital allocation We develop a theoretical model to take thisbehavior into account and empirically show that this extended model can betterpredict the limit order placement behavior than previous models suggested in theliteratureIn Chapter 7 Reaktionen der Kryptowaumlhrungsmaumlrkte auf die COVID-19-Pandemie weempirically test how the outbreak of the COVID-19 pandemic affects cryptocur-rencies By constructing two cryptocurrency portfolios based on market capital-ization we show that smaller cryptocurrencies react differently to this global cri-sis and perform superior compared to larger ones since the outbreak Further weemploy a fixed-effects regression model and find empirical evidence for a relation-ship between prior trading volume and returns and autocorrelation of returns incryptocurrency markets Moreover the intertemporal relation between past tradingvolume and current returns seems to be enhanced for smaller cryptocurrencies sinceCOVID-19 is shaping the macroeconomic development


The studies conducted in this dissertation are performed in different market environ-ments (see Table 11) allowing us to get comprehensive insights into the behaviorand mechanisms of competitive markets We study the impact of different marketfrictions to better understand how capital can be efficiently allocated and this dis-sertation aims to contribute to the academic literature by providing new empiricalevidenceWhile all studies conducted in this dissertation are independent of each other theyhave an important common denominator as they are connected via the concepts ofmarket efficiency economic behavior and information asymmetry and each chapterhighlights a different aspect of these crucial concepts Table 12 provides an overviewof the framework in which the three aspects and their features are addressed in eachof the following chapters Table 11 summarizes the market conditions and data us-age in each chapter From Chapter 2 to Chapter 6 the observation period steadilydecreases while the data granularity increases While we first analyze establishedstock markets (Chapter 2 and Chapter 3) we later turn to the global emerging mar-ket for cryptocurrencies (Chapter 4 to Chapter 7) and draw parallels between thesetwo markets

TABLE 11 Research Setting and Data

Notes This table shows the research setting of each chapter of this dissertation and meta informationabout the empirical data analyzed

Chapter Region Market Environment Observation Period Data Granularity

2 Global Established Decades AnnualMonthly3 EUampUS EstablishedIn change Years MonthlyDaily4 Global Young Months MonthsDaily5 Global Young Months MonthsDaily6 Global Young Days DailySeconds7 Global YoungIn change Months Daily


TABLE 12 Research Focus across Chapters

Notes This table shows the research topics of each chapter of this dissertation based on three differentaspects market efficiency investor behavior and information asymmetry

Chapter Agents Market Efficiency Investor Behavior Information Asymmetry

2 bull Investor bull Compatibility with bull Social responsibility bull Between management inter-bull Company social responsibility ests and shareholders

3 bull Investor bull Efficiency under bull Reaction to new bull Between investors andbull Company regulatory constraints regulation analystsbull Analyst bull Competition between bull Across investors

analysts

4 bull Investor bull Transparency bull ICO pricing bull Between issuing companybull Company bull Underpricing bull Information policy and investors

bull Across investors

5 bull Investor bullMarket impact of bull Implied demand bull Across investorsLOB characteristics and supply

6 bull Investor bull Rationality of bull Order placement bull Across investorsinvestors behavior

7 bull Investor bull Predictability bull Flight to save -bull Company bull Efficiency during crisis havens

8

Chapter 2

What do we know about sociallyresponsible investments

Chapter 2 has been published as a journal articleHaumlfner David Florian Kiesel and Lucas Wirthmann (2017) What do we knowabout socially responsible investments In Zeitschrift fuumlr Umweltpolitik und Umwelt-recht (4) pp 299ndash331 ISSN 0931-0983

9

Chapter 3

The Role of Investment Researchin view of MiFID II An EmpiricalAnalysis of InformationAsymmetry Idiosyncratic Risk andLiquidity

The basic idea of regulation in financial markets is to improve the market qualityIn this paper we employ a difference-in-differences approach by using daily dataof 1646 US firms and 1281 EU firms to investigate the influence of the EU-wideimplementation of the EU Markets in Financial Instruments Directive (MiFID II) inJanuary 2018 MiFID II enforces that investment research provided by financial ana-lysts explicitly has to be paid for by investors Market participants expected a reduc-tion in demand for research services and as a result more information asymmetriesin European stock markets Consistent with these expectations we find that the im-plementation of MiFID II has led to a significant decline in stock liquidity and anincrease in stocksrsquo idiosyncratic riskIn a second step we examine the effect of a change in investment research cover-age before and after the implementation of MiFID II We find that an increase in thenumber of analysts covering a stock significantly affects information asymmetry andidiosyncratic risk supporting the idea that analysts act as a source of information forinvestors We show that both effects are amplified by the introduction of MiFID II

31 Introduction

Financial market regulation aims to direct the market development towards an eco-nomically preferable condition where capital supply and price stability is guaran-teed However the approach and the extent to which regulation is implemented

Chapter 3 The Role of Investment Research in view of MiFID II An EmpiricalAnalysis of Information Asymmetry Idiosyncratic Risk and Liquidity

10

bears many risks as complying with more directives comes along with an addi-tional financial burden for affected companies In a globalized world competingjurisdictions need to be taken into account as well as capital is not bound to nationalborders and investors may leave if the legal environment is too restrictive Henceregulation needs to be reasonable and only be implemented if severe market ineffi-ciencies predominateFocusing on the impact of regulation Thomsen and Vinten (2014) distinguish be-tween two different hypotheses concerning the costs and benefits of investor pro-tection regulation The efficiency hypothesis states that benefits of investor protec-tion regulation outweighs the costs leading to the implementation of new regula-tion to improve the stock markets For this reason regulation would likely have apositive effect on company performance and stock prices On the other hand theover-regulation hypothesis postulates that regulation originates from rent seekingfrom powerful economic players which follow their own financial interests leadingto costs which exceed the benefits of regulation (Thomsen and Vinten 2014 p 800)While the authors focus on the influence of regulation on company delistings theircost-benefit hypothesis can be transferred to any influence of regulating capital mar-ketsInformation asymmetry between investors and capital seekers describes one formof market failure and can have a serious impact on trading costs Therefore markettransparency is an important objective for policy makersBased on this theoretical background we study the impact of the revised EU Mar-kets in Financial Instruments Directive (MiFID II) on financial markets As of Jan-uary 2018 MiFID II forces inter alia that investment research has to be billed ex-plicitly In this paper we study the effect of MiFID II on European financial marketsby employing a difference-in-differences approach around the implementation dayof the new regulation studying the impact of MiFID II on investment research cov-erage liquidity and idiosyncratic risk In a second step we study the relationshipbetween investment research coverage and information asymmetry and the impactof MiFID II on this relationship We hypothesize that MiFID II increased the compe-tition between financial analysts Financial analysts now have to proof their worthwhich should increase the quality of their work and lead to a higher informativenessof stock prices We find that analyst coverage did not decrease significantly in theshort term However the amount of coverage overall declined in the medium termMiFID II further led to a significant decrease in stock liquidity and an increase in thebid-ask spread idiosyncratic risk and the level of asymmetric information We alsofind that MiFID II affects the informational role research coverage has on the bid-askspread and idiosyncratic risk by employing a fixed effects regression modelThe remainder of this paper is structured as follows Section 32 provides an overviewof investment research regulation and compares the treatment of investment re-search in the European jurisdiction to the regulatory framework in the United States


11

Section 33 gives a literature review on the theoretical framework regarding the roleof financial analysts in capital markets Section 34 defines the terms liquidity in-formation asymmetry and idiosyncratic risk and introduces empirical measures forthese theoretical concepts Section 35 describes the data which is used in our empir-ical analysis conducted in Section 36 Section 37 concludes

32 Regulation of Investment Research

In this section we focus on the regulatory treatment of investment research We high-light the historic way that led to the introduction of MiFID II and compare EU andUS approaches to regulate investment research We find the regulatory frameworkof the United States to be comparable to the pre-MiFID II European framework mak-ing US stocks suitable for the control group of our subsequent empirical analysis

321 Investment Research in the European Union

The first Markets in Financial Instruments Directive (MiFID I) was introduced acrossall member states of the European Economic Area on 31st of November 2007 Theaim of MiFID I was to facilitate cross-border trading for private and institutional in-vestors Further the European Commission tried to achieve a harmonization acrossEuropean trading venues As initially discussed the superior regulatory aim of Mi-FID I was to benefit the economy by lowering the overall cost of capital In orderto be reasonable this effect must exceed the costs of complying with new regula-tory requirements MiFID I led to a significant regulatory overhaul in Europe andis widely regarded as one of the most significant regulatory changes for financialmarkets (Casey and Lannoo 2009 and Ferrarini and Wymeersch 2006)Since 3rd of January 2018 the revised EU-wide Markets in Financial InstrumentsDirective (MiFID II) is in force aiming to increase the transparency and the investorprotection in the European financial markets MiFID II replaces MiFID I and in-volves regulatory changes targeting investment research Notably investment re-search provided by brokers in the form of stock analysis research reports and accessto the management of covered firms now has to be paid for by potential investorsexplicitly ndash a change which has drawn substantial media attention For the first timethe exact monetary value of investment research has to be determined disruptingthe market for investment research services Prior to MiFID II investment researchwas commonly provided by brokers free of charge with the intention to encourageinvestors to engage in trading Providing investment research has therefore beenpart of the overall brokerage service providing investors with information on theone hand and serving as a marketing tool on the other hand The actual costs for cre-ating investment research have been usually cross-subsidized via the broker marginof subsequent trades As a consequence it was almost impossible for investors to


12

keep track of how much they actually spend for investment research This procedurehas been deemed to be too intransparent by EU regulators and is prohibited sinceMiFID II is in effect Consequently investment research has to be priced explicitlysince 3rd of January 2018 raising questions about the monetary value of investmentresearch for brokers and investorsUncertainty remains on how the new regulation affects the availability and qualityof research One of the main concerns with the implementation of MiFID II is its ef-fect on the overall research coverage of companies Especially small companies maysuffer from an increase in illiquidity caused by a decrease in the number of researchanalysts The investorsrsquo demand for research considering those firms might be toosmall to be profitable for research providers The general belief is that the overallsupply of investment research decreases leading to a lower level of analyst coverageand hence to a decrease in liquidity (Deutsche Boumlrse 2017) Smaller listed companieshave been anticipated to be the most affected by MiFID II as they already suffer froma low analyst coverage which potentially drops to zero due to a lack of investor in-terest (Deutscher Investor Relations Verband 2017) Providing some early empiricalevidence Fang et al (2019) find a decline in analyst coverage and show that recom-mendations of remaining analysts receive more market attention and have greaterinformation content The authors further find analystsrsquo participation in earnings-conference calls and the number of questions asked to have increased since MiFIDII A survey conducted by the CFA institute also finds that the market place for re-search has become more competitive and 44 of sell-side respondents believe thatthe quality of investment research has declined while buy-side respondents do notsee a change in research quality (Preece 2019) Further 47 of buy-side and 53of sell-side participants respond with a decrease in coverage for small- and mid-capstocks since MiFID II is in placeFrom a theoretical point of view market participants need to know whether theirsource of investment research possesses private information on which trading prof-its can be generated as only then it would be worth to pay for it1 With the newregulatory framework in place investors are incentivized to learn about the skillof financial analysts as they face obvious monetary losses if they base their invest-ments on paid research that does not contain private information We hypothesizethat this relationship leads to an extinction of poorly performing financial analystsas investors will pick their source of information more carefully We argue that thischange should be measurable through a change in the impact of financial analystson stock specific characteristics Consequently we would not expect any measurableeffect of analyst coverage at all if financial analysts are not able to create or unveilprivate information with their work

1Note that the amount which is paid for investment research can be interpreted as the cost ofinformation in the model suggested by Grossman and Stiglitz (1980)


13

Generally we suppose that an increase in analyst coverage decreases the informa-tion asymmetry of a stock as the competition between analysts increases This islowering the price for which analysts are willing to disclose their information ul-timately increasing transparency We expect that MiFID II amplifies this effect asfinancial analysts have to demonstrate their value to investors We would also ex-pect the overall amount of analyst coverage to drop due to MiFID II especially forsmaller companies as it is likely less profitable to cover them

322 Investment Research in the United States

The treatment of investment research in the US is regulated in the Investment Ad-visers Act of 1940 Section 202(a)(11) defines an investment adviser as any personwho for compensation engages in the business of advising others either directlyor through publications or writings as to the value of securities or as to the advis-ability of investing in purchasing or selling securities or who for compensationand as part of a regular business issues or promulgates analyses or reports concern-ing securities However Section 202(a)(11)(C) of the Investment Advisers Act of1940 specifically excludes any broker-dealer whose performance of such servicesis solely incidental to the conduct of his business as a broker or dealer and whoreceives no special compensation therefor The US Securities and Exchange Com-mission (SEC) generally treats providing research as the provision of investmentadvice Though Section 202(a)(11)(C) exempts broker-dealers from treatment as in-vestment advisers ldquosince broker-dealers that provide research generally include thecost of that research in the commissions they charge for execution of securities trans-actions (Johnsen and Grady 2017)2 In the jurisdiction of the US this leads to thedistribution of investment research to investors seemingly free of charge with com-pensation in the form of soft dollars which is similar to the handling in the EU priorto MiFID IIMiFID II runs counter to SEC regulations as it requires investment research to bepaid for in hard dollars Under current SEC regulations MiFID II would force allproviders of cross-border research to register as an investment adviser in the USFor this reason the SEC Division of Investment Management issued a No-ActionLetter in October 2017 stating that it will not recommend enforcement action if aUS broker-dealer provides research to an EU investment manager that is required topay for research services (SEC 2017) The relief is granted for a temporary period of30 months from MiFID IIrsquos implementation date

2Registering as investment adviser in the US imposes multiple burdens for research providers com-plicating the dissemination of research eg registered investment advisers need to take on extra fidu-ciary responsibilities impacting large parts of how the research business is currently done


14

33 The Role of Investment Research in Capital Markets

In an attempt to explain the impossibility of informational efficiency in capital mar-kets Grossman and Stiglitz (1980) propose a noisy rational expectations equilibriummodel In their model prices only partly reflect the information of informed tradersimplying that those who expend resources to obtain information will also be able togenerate profit off of this information In their model prices act as a delivery systemof information from informed to uninformed traders As investors can only profitfrom their superior information advantage by trading they inevitably reveal theirinformation to uninformed traders through the resulting price movement of the re-spective asset The authors hypothesize that information can be noisy ie marketsare not fully efficient Their model leads to an information asymmetry between twodistinct groups of market participants informed and uninformed (noise) tradersGrossman and Stiglitz (1980) show that the market price will reveal most of the infor-mation of informed traders when information is cheap or precise Prices howevercannot completely reflect the information which is available to the informed tradersbecause in that case those who paid for the information or created it cannot benefitfrom the information as it would already be fully incorporated in the market priceTransferred to our problem setting we hypothesize that analysts might be able tooffer private information by creating investment research and market participantscan decide to buy this informationNevertheless disagreement persists in the literature regarding the actual role of in-vestment research produced by financial analysts While one strain of literature pro-vides evidence that financial analysts actually produce and provide valuable infor-mation (eg Womack 1996 Barber et al 2001 Gleason and Lee (2003) Kelly andLjungqvist 2012 Piotroski and Roulstone 2004) other studies present findings in-dicating that investment research analysts do not seem to be able to create privateinformation (eg Easley OrsquoHara and Paperman 1998)Above all this stands a well known information paradox regarding the existenceof informational market efficiency If all information is already incorporated in themarket price information itself has no financial value Consequently market par-ticipants would not gather new information which would lead to the case of newinformation not being reflected in market prices In this stage markets can not beinformation-efficient as it would be beneficial to gather private information againleading to the paradoxFocused on the role of research analysts French and Roll (1986) hypothesize thatmore volatile prices during exchange trading hours could be caused by the fre-quency of information arrival during business days The authors further state thatpublic information affects prices before anyone can trade on it eg weather whileprivate information is produced by investors and security analysts The authors ar-gue that more private information might occur while security analysts actively scan


15

company documents and thereby reveal previously unknown information Thiscould also explain the observation of higher volatility during business days To fur-ther analyze the behavior of stock return variances French and Roll (1986) split dailystock returns into a rational information component a mispricing component and abid-ask error Although they identify mispricing errors they state that the magni-tude of these errors is too small to explain the difference in variances during tradingand non-trading days The authors attribute the observed effect to differences in theflow of information during trading and non-trading hours Groundbreaking workin the analysis of the intrinsic value of stock research reports has also been con-ducted by De Bondt and Thaler (1990) Studying security analyst behavior the au-thors use analystsrsquo earnings forecasts to determine whether forecasted changes aretoo extreme and whether the prediction bias grows with uncertainty ie when thepredicted changes range into the more distant future De Bondt and Thaler (1990)argue that the question of distortedness in professional recommendations is espe-cially important as most investors neither have the time nor the necessary skill toproduce independent predictions and therefore depend on buying earnings fore-casts The authors try to predict the actual change in earnings per share using theforecasted change as an explanatory variable which ndash imposing efficient markets ndashshould not be possible They find evidence for excessive optimism and concludethat the findings can be attributed to an agency problem arising from analysts whowork for broker houses and make money by encouraging trading However theyquestion if this agency problem can be considered the sole reason for the provenoverreaction as similar patterns can be observed in scenarios where no agency con-flict is present Womack (1996) analyzes the influence of buy and sell recommen-dations issued by investment research analysts on stock prices As those recom-mendations originate from predictions of stock values which should incorporate allindustry and firm-specific information Womack (1996) states that they allow to di-rectly test whether informed investors can outperform the stock market He ob-serves permanent changes following a recommendation which indicates that rec-ommendations include private information He also observes that buy recommen-dations occur more frequently than sell recommendations Together these findingswould legitimate the compensation of brokerage firms3 The author further analyzesstock prices and company-specific recommendations during the 1989-1991 time pe-riod and categorizes all recommendations into ldquobuyrdquo ldquoholdrdquo and ldquosellrdquo and findscumulative average abnormal returns of 400 for a stock added to the ldquobuyrdquo listin a three-day event window In contrast the cumulative average return of a stockadded to the ldquosellrdquo list is -432 in the same time frame This finding indicates thatstock prices are influenced by analyst recommendations Womack (1996) also findsevidence that this influence persists over the long term and that the market reaction

3The compensation for providing investment research services is traditionally earned by soft dollarcommissions


16

is significantly larger for firms with a smaller market capitalization Easley OrsquoHaraand Paperman (1998) further investigate the role of analysts in financial markets asdisagreement between previous studies persists French and Roll (1986) as well asEasley OrsquoHara and Paperman (1998) suppose that private information exists in themarket and financial analysts might uncover private information with their workHowever the worth of a financial analystrsquos research output can range from a stockselling marketing tool up to an exploitable information edge The latter case impliesthat financial analysts possess valuable private information Easley OrsquoHara andPaperman (1998) try to investigate the informational role of financial analysts byestimating the probability of information-based trading using trade data of NYSEstocks They further investigate whether the amount of analyst coverage on a firmlevel increases the likelihood of a private information disclosure of a company Bycomputing the probability of informed trading their technique allows to investigatewhether analysts create private information Surprisingly they find that a companythat is highly covered by analysts has a lower risk of information-based tradingHowever the amount of information-based trades is higher This phenomenon canbe explained by a high number of noise traders trading these stocks diluting theoverall risk of facing an information-based trade Easley OrsquoHara and Paperman(1998) observe that the probability of private information events does not depend onthe number of analysts covering a stock Calculating the probability of information-based trading for each stock in the sample and regressing stock spreads on the num-ber of analysts covering the stock they show that analysts do not appear to createprivate information These results contradict the findings of Womack (1996)More recently Wallmeier (2005) examines analystsrsquo earnings forecasts for GermanDAX100 firms during the stock market boom of the 1990s using five alternative fore-casting models Based on IBES data from 1991 to 2000 Wallmeier (2005) testswhether the aggregated recommendations of analysts are too optimistic The authordefines the forecasting error of a firm as the difference between the predicted andthe actual book equity rates of return By removing the forecasting error ie theempirically observed tendency of too optimistic stock recommendations Wallmeier(2005) is able to outperform five alternative benchmark models This result indicatesthat earnings forecasts contain information although the market level of earningsis valued too generous by analysts Wallmeier (2005) also investigates whether theoptimistic bias diminishes over the long term and finds evidence for a decline of thebias over time Bessler and Stanzel (2007) analyze the quality and efficiency of earn-ings forecasts of analysts in the German stock market They attribute a central role inthe information efficiency of capital markets to the research carried out by financialanalysts However to guarantee an efficient allocation of resources they argue thatfinancial research needs to be free of conflicts of interests Further it is crucial thatregulation supports rather than restricts financial research eg by reducing poten-tial conflicts of interest Bessler and Stanzel (2007) analyze the German stock market


17

from 1995 to 2004 and find positive and biased analyst forecasts They argue thatthe persistence of this bias could be explained by differences in the publicity and ac-counting regulation of different firms which affects the number of analysts followinga company and the overall quality of available information Additionally behavior-oriented factors could influence the precision and overall quality of predictions egbusiness relations with the management of the analyzed company Benchmarkingagainst a naiumlve forecast Bessler and Stanzel (2007) use a straightforward approachto access the forecasting error of financial analysts The authors compute the averagerelative forecasting error for analyst forecasts and compare the results with the errora naiumlve prediction would produce on a fiscal year basis Kelly and Ljungqvist (2012)state that analysts are among the most influential information producers in financialmarkets Using the closure of research departments of forty-three brokerage firmsas an exogenous source of variation in the extent of analyst coverage the authorsemploy a difference-in-differences approach to assess the influence of a decrease inanalyst coverage on multiple liquidity proxies Most notably the authors take care toonly consider exogenous changes to analyst coverage in order to prevent endogene-ity concerns They further investigate whether a loss of analyst coverage affects theprice of the respective stock Providing evidence that coverage terminations increaseinformation asymmetry they also find that information asymmetry has a substantialeffect on asset prices and identify liquidity as the primary link between asset pricesand information asymmetryIn summary the link between investment research conducted by financial analystshas been broadly studied using a multitude of different approaches The generalobjective of these studies is to understand which role investment research analystsplay in financial markets and how analyst coverage affects the interplay betweenliquidity returns information asymmetry and market efficiencyWe contribute to the literature in the following sections by empirically examining theinfluence of analyst coverage on information asymmetry and the effect of putting aconcrete price on investment research as is stipulated by MiFID II We show thatanalyst coverage affects information asymmetry leading to the conclusion that thelevel of information asymmetry can be linked to the number of analysts coveringa stock and that research analysts possess and distribute private information Wealso show that the role and impact of financial analysts in European stock marketschanged since the implementation of MiFID II

34 Variable Definition

Efficient markets are characterized by certain desirable features high liquidity lowinformation asymmetry and low transaction costs An efficient market guaranteesthe efficient allocation of capital and supports the general economy A high level ofliquidity allows all market participants to trade anytime at low trading costs while


18

only marginally affecting asset prices Market efficiency also requires that all avail-able information is reflected in market prices ruling out information asymmetryTherefore measuring the existence and dependencies of information asymmetryand changes in liquidity is important to understand the prevailing level of marketefficiency and the impact of MiFID II

341 Illiquidity

In the literature there are many liquidity measures proposed which can be dividedinto two major groups price-based liquidity measures and volume-based liquiditymeasures We choose to focus on the volume-based illiquidity measure proposedby Amihud (2002) as it is widely used in the literature and is straight forward tocomputeAs stated above a key characteristic of liquid markets is the possibility to trade anasset without heavily impacting the asset price which implies that asset prices arenot easily manipulated by large trades This necessity of a liquid market makesit important to analyze by how much the number or size of transactions affectsthe asset price By setting the daily trading volume into ratio with daily returnsthe illiquidity measure proposed by Amihud (2002) allows to directly assess thiskey market characteristic Amihud (2002) shows that expected stock returns are anincreasing function of expected stock illiquidity supporting the illiquidity premiumhypothesis The author also shows that the effect of expected illiquidity on expectedstock returns is higher for smaller stocksBased on Amihud (2002) we compute the illiquidity measure as a proxy for stockilliquidity according to the following formula

Illiquidityit =|Rit|

VOLit(31)

Illiquidityit describes the Amihud (2002) illiquidity measure defined as the ratioof the absolute stock return (|Rit|) to the trading volume in US Dollar (VOLit) forstock i on trading day t Illiquidityit directly incorporates the trading volume of astock and can be interpreted as the change in stock price per unit of trading vol-ume Amihud (2002) also provides an alternative interpretation of his measure Ifinvestors agree about the implication of new information the stock price changeswithout trading while disagreement leads to an increase in trading volume ThusIlliquidityit can also be interpreted as a measure of consensus belief among investorsabout new information (Amihud 2002 p 43) While not mechanically induced theilliquidity measure should be positively correlated with the bid-ask spread from aneconomic perspective as illiquidity is associated with a high bid-ask spread and alow trading volume The return should be higher for illiquid stocks too as investorsdemand a liquidity premium for holding stocks that are harder to convert into cash


19

342 The Bid-Ask Spread

We compute the bid-ask spread as the difference between the best ask price askit

and the best bid price bidit for stock i at closing of trading day t

Absolute Spreadit = askit minus bidit (32)

We also compute the relative bid-ask spread as follows

Relative Spreadit =askit minus bidit

12 lowast (askit + bidit)

(33)

The denominator can be interpreted as the mid price of stock i at closing of tradingday t The relative bid-ask spread normalizes the size of the bid-ask spread by therespective share priceThe bid-ask spread resembles a widely used proxy for information asymmetry Cope-land and Galai (1983) state that a dealer or market maker sets the size of the bid-askspread in order to maximize his own profits By increasing the bid-ask spread themarket maker faces a trade-off between losing expected revenue from trades withliquidity (noise) traders and protecting himself from losses caused by trades againstinformed investors Chung et al (1995) propose two contrary hypotheses regardingthe relationship between financial analysts and the bid-ask spread On the one handit might be beneficial for analysts to cover stocks with a high level of informationasymmetry Market makers would then observe the number of analysts covering astock and increase the bid-ask spread for highly covered stocks to protect themselvesagainst trading with informed investors On the other hand the likelihood of insidertrading will be reduced if the firm is followed by many financial analysts because an-alysts reveal most of the firm-specific information to investors and market makersIn this case analyst coverage should decrease the bid-ask spread The authors arguethat determining which theory is more realistic needs to be tested empirically Usinga simultaneous equation regression analysis and IBES data Chung et al (1995)find empirical evidence for a bidirectional relationship between the bid-ask spreadand the number of analysts covering a stock and support for the first hypothesisGlosten and Milgrom (1985) state that the problem of matching buyers and sellersis particularly eminent in trading shares of small companies The authors propose atheoretical model based on the idea that even under the assumption that a marketmakerrsquos cost of trading as well as his expected returns are zero a bid-ask spread canemerge purely due to informational reasons In the proposed model informed anduninformed investors as well as the market maker are risk-neutral Glosten and Mil-grom (1985) describe an adverse selection problem which is based on the informa-tion disparity between traders and market makers In their model informed traderspossess some kind of private information or superior analysis skills they trade onThe authors show that adverse selection by itself can be responsible for the existence


20

of a bid-ask spread The size of the bid-ask spread depends on the proportion ofinformed and uninformed investors as well as the quality of private informationGlosten (1987) more distinctively separates the bid-ask spread into two componentsOne part caused by exogenous costs for the market maker and the other part causedby informational asymmetry If market makers have to compete for the best bid-ask spread the magnitude of the bid-ask spread is mainly determined by the levelof information asymmetry in the market4 Kim and Verrecchia (1994) show thatmarket makers lose by trading against informed traders (Copeland and Galai 1983and Glosten and Milgrom 1985) A higher level of information asymmetry causesmarket makers to increase the bid-ask spread in order to recover from these lossesThese findings make the bid-ask spread a suitable proxy for information asymmetryHowever by decomposing the bid-ask spread into three cost components Stoll (1989)suggests that the bid-ask spread is also determined by inventory holding costs andorder processing costs5

Spread = Adverse Information Costs + Inventory Holding Costs + Order Processing Costs

Based on this theoretical model the bid-ask spread would also change when orderprocessing costs or inventory holding costs changeWhile we can not observe inventory holding costs directly we can observe stockilliquidity by computing the illiquidity measure proposed by Amihud (2002) anduse it as a proxy for inventory holding costs Illiquidity and inventory holding costsshould be highly correlated as illiquid stocks have high inventory holding costs andvice versaIt is well documented that less frequently traded stocks typically possess a higherbid-ask spread Easley Kiefer et al (1996) offer multiple explanations for largespreads First an investor may not be able to liquidate an infrequently traded stockthus demanding an illiquidity premium for compensation Moreover the risk offacing an information-based trade might be higher in illiquid stocks In this case alarger bid-ask spread would compensate for the risk of facing an informed counter-partyWhile order processing costs theoretically co-determine the size of the bid-ask spreadwe argue that the impact is neglectable in modern financial markets Since the for-malization of the first theoretical models explaining the bid-ask spread (Stoll 1989)exchanges have become more cost-efficient due to automatization of the order exe-cution process Hence we choose to exclude order processing costs from our furtherexamination In our proposed empirical fixed effects model it is even sufficient to

4In the extreme case of perfect competition market makers would be forced to aim for zero exoge-nous costs to stay competitive

5Stoll (1989) develops and empirically tests a model to derive the relative composition of the quotedspread He shows that adverse information costs account for 43 inventory holding costs for 10 andorder processing costs for 47 of the total quoted spread


21

assume that order processing costs remain stable during the observation period Us-ing fixed effects our results are unbiased even if order processing costs are not zeroas long as they do not significantly vary during the observation period In the case oftime-constant order processing costs we get rid of the influence of order processingcosts on the bid-ask spread as it is part of the time-constant error term6

343 Stock Price Informativeness and Idiosyncratic Risk

Recent findings in the literature suggest that idiosyncratic risk is related to stockprice informativeness (eg Morck Yeung and Yu 2000 and Chen Goldstein andJiang 2006) however it is not a priori clear whether higher idiosyncratic risk impliesmore or less informed stock pricing (Durnev et al 2003)Roll (1988) points out the problem suspecting a link between a low value of R2 inasset pricing models and the existence of private information Not finding improve-ments in R2 by controlling for public news events the author concludes that thefirm-specific return variation might be caused by traders acting on private informa-tion Roll (1988) declares that there are two possible contradictory explanations forhis findings The first explanation proposes that firm-specific price variation reflectsthe incorporation of private information into prices The second explanation statesthat firm-specific return variation might actually reflect noise trading Durnev et al(2003) conclude that this problem is an empirical question and several subsequentstudies emerged supporting either the first or the latter viewPiotroski and Roulstone (2004) link idiosyncratic risk to the role of financial analystsby investigating the influence of trading and trade-generating activities of informedmarket participants They measure the firm-specific industry-level and market-level information impounded into stock prices as measured by stock return syn-chronicity

SYNCHi = ln(

R2i

1minus R2i

)(34)

R2 is the part of the volatility of stock i at time t which can be explained by marketvolatility ie the systematic risk component Consequently 1minus R2 resembles theunsystematic (idiosyncratic) risk R2 can be estimated via OLS by computing thegoodness-of-fit of the baseline market model for each stock individually

Rt = α + βRmt + εt (35)

Rt is the daily stock return of a stock on day t and Rmt is the daily market returnHence stock return synchronicity is a measure of the extent to which market returnsare able to explain firm specific returns Piotroski and Roulstone (2004) hypoth-esize that informed parties (notably analysts institutional investors and insiders)

6In addition we would not expect to introduce an omitted variable bias as to our knowledge thereis no direct link between analyst coverage and order processing costs


22

contribute or disseminate information into the price formation process in differentways which should lead to a systematic variation in stock return synchronicity withthe presence or absence of these partyrsquos activities (Piotroski and Roulstone 2004p 1120) The authors state that analysts convey their information through earningsforecasts and investment recommendations concluding that analysts can improvethe price efficiency by dissemination information into the price formation process ofcovered firms As there is empirical evidence that analyst activities trigger trades(eg Givoly and Lakonishok 1979) Piotroski and Roulstone (2004) argue that an-alyst activity should cause prices to reflect this additional information resulting ingreater stock return synchronicityMore recently a slightly modified measure has been established in the literatureZhu Jog and Otchere (2014) use the following measure for idiosyncratic risk whichis also based on the market model

Ψi = ln(

1minus R2i

R2i

)(36)

Note that Ψit and SYNCHit are negatively correlated by definition Both measuresare log-transformed to create an unbound continuous dependent variable with amore normal distribution as both R2 and 1minus R2 are bound between zero and oneStudying the influence of analyst coverage and MiFID II on idiosyncratic risk bearsthe advantage of obtaining a measure that is linked to information asymmetry andwhich is derived without a direct link to the bid-ask spread This allows us to add tothe literature regarding the role of idiosyncratic risk as a proxy for informativenessof stock prices and also serves as a robustness check for our empirical results shownin Section 36 Following the argumentation of Piotroski and Roulstone (2004) wewould expect that an increase in analyst coverage leads to a decrease in the idiosyn-cratic risk measure Ψ We hypothesize that MiFID II increased competition betweenanalysts to provide economically valuable information and therefore amplifies thiseffect Considering the role of financial analysts disseminating private informationit is worth to note that information may not be strictly private or public Initiallyprivate information becomes more public as more uninformed investors becomeinformed by either learning about the information from trading or by directly buy-ing the information when the costs to access the information decrease over time7

35 Data

We use Thomson Reuters Datastream to obtain a data set consisting of daily data ofpublicly traded stocks covered by the Thomson Reuters Global Equity Index from

7A similar reasoning is given by Chung et al (1995) who argue that an analyst will first inform hisfavored clients about a new piece of information before putting it in a newsletter and finally disclosingit publicly (Chung et al 1995 p 1028)


23

each trading day in the months October 2017 November 2017 February 2018 andMarch 2018 We examine the constituents of the Thomson Reuters Global EquityIndex in order to obtain a representative sample of the stock market universe TheThomson Reuters Global Equity Index consists of more than 8000 publicly tradedcompanies across 51 countries and is designed to serve as a broad market bench-mark and to track the performance of liquid equities worldwide For our analysiswe use the companies listed in this index A major advantage of this index is that itis designed to incorporate more than 9950 of the market capitalization of all liquidstocks8

We use the International Securities Identification Number (ISIN) to determine inwhich country a company is listed and drop all companies from our sample whichare not listed in EU28 member states or the United States We further exclude allfinancial services companies identified by the respective 2-digit Standard IndustrialClassification Code (SIC) from our sample Consequently all remaining companiesare either listed in EU28 member states or the US the first defining our treatmentgroup and the latter defining our control groupSimilar to Chung et al (1995) and Easley OrsquoHara and Paperman (1998) we use thenumber of earnings-per-share (EPS) estimates reported by the Institutional BrokersrsquoEstimate System (IBES)9 in order to assess the investment research coverage of astock We find that if a stock is covered by an analyst EPS is one of the most reportedvariables and the number of EPS estimates should therefore provide a reasonableproxy for the amount of investment research coverage of a company Following theliterature we use the number of EPS estimates reported as a proxy for investmentresearch coverage (eg Piotroski and Roulstone 2004) We further gather informa-tion about the market capitalization of a company daily stock trading volume andthe market closing bid and ask prices using Thomson Reuters DatastreamOur final sample consists of four months of daily data covering 2927 companiesthereof 1281 European companies and 1646 companies listed in the United StatesThe five largest economies (United Kingdom Sweden France Germany and Italy)account for more than half of all European observations and the United States ac-count for more than half of the total observations in the sample A detailed break-down of our sample divided by country can be found in Table 31

8Equity closed funds equity derivatives exchange traded funds some units investment trustsand Limited PartnershipsMaster Limited Partnerships are excluded Further in order to be eligibleconstituents must have a market capitalization of at least USD 150mn This restriction should not raiseconcerns as the median company listed in the German small cap index SDAX already has a marketvalue which is four times higher

9Thomson Reuters IBES Estimates provides sell side analyst estimates for a companyrsquos futurequarterly and annual financial performance including real-time and historical estimates from 900 con-tributors across 100 developed and emerging markets totaling over 13000 individual analysts Thom-son Reuters maintains a close relationship with sell-side contributors who must pass a rigorous screen-ing process before their research gets accepted Coverage includes 99 of MSCI Asia 98 of MSCIWorld and 100 of SampP500 resulting in a coverage total of over 22000 active companies (+ 20000inactive) across over 87 countries (httpsdevelopersthomsonreuterscomcontentibes-estimates)


24

As we are mainly interested in persistent effects of MiFID II we focus on comparingdaily trading data from before and after the implementation of MiFID II10 Doingso we face a trade-off between minimizing the potential threat of introducing mea-surement errors by measuring effects not caused by MiFID II and making sure thatEU markets had enough time to adjust to the new market conditions We increasethe robustness of our analysis by estimating results for two different time framesTable 32 provides summary statistics for the final sample used for our subsequentempirical analysis in Section 36 It can be seen that the average characteristics of thetreatment and control group barely differ

351 Descriptive Statistics

In this section we highlight several properties to get an insight into statistical depen-dencies in our sample First we examine the dependency between the market sizeof a company and the number of analysts following the companyrsquos stock by com-puting the average number of analysts covering a firm dependent on the marketcapitalization (in USD) We find research coverage to be highly correlated with firmsize (Figure 31) An evident explanation would be that larger companies generallygain more market exposure and publicity therefore demand for investment researchand consequently investment research coverage is larger for those firms Moreovercertain companies might be too small to attract large investors as there are just notenough shares available to purchase making these stocks uninteresting Figure 31depicts the mean number of analysts per stock for quartiles of market capitaliza-tion with Q1 being composed of companies with the smallest market capitalizationInterestingly investment research coverage increases almost steadily between thethree smallest quantiles of market capitalization (from Q1 to Q2 + 296 analystsfrom Q2 to Q3 + 292 analysts) while a company in the highest quantile is coveredby 709 more analysts than a company in the second largest quantile on averageA possible explanation for this finding might be that there exist some must havecompanies which most of the analysts cover independent of their own research fo-cusWe also examine the empirical distribution of research coverage and find that on av-erage a firm is covered by 1041 analysts across our data We further observe thatthe distribution of analyst coverage reaches a maximum at four analysts Based onthe QQ-plot shown in Figure 32 analyst coverage seems to be reasonably well fit-ted by a log-normal distribution It appears that there are only few companies which

10Analyzing short term effects of MiFID II could be achieved by conducting an event study aroundthe implementation day However this approach would be prone to confounding events around thefiscal year change However we do not find confounding international events during the estimationwindows which arguably affect the US and the EU in a different way


25

Notes This figure shows the average number of analyst coverage for four quantiles of market capi-talization (Q1 0minus 25 Q2 25minus 50 Q3 50minus 75 Q4 75minus 100) Market capitalization ismeasured in US Dollar for all 2927 observed firms

FIGURE 31 Research Coverage dependent on Market Capitalization

Notes This figure shows the empirical distribution of analyst coverage per firm (left) and the QQ-plotof the logarithmized analyst coverage (right)

FIGURE 32 Empirical Distribution of Research Coverage per Firm


26

are covered by a high amount of analysts11 These findings also provide some sup-port for the hypothesis that there might be some saturation regarding the amount ofavailable research coverage ie an analyst might face a trade-off between the diffi-culty to unveil new information about a stock which has already been analyzed bycompeting analysts on the one hand and the investor demand for his research on theother hand In this case the analyst has to weigh the positive effect of covering ahighly sought after stock against the increased difficulty to unveil private informa-tion about this stockFigure 33 depicts the empirical distribution of the bid-ask spread and the relativebid-ask spread in our sample which we compute based on Formula 32 and For-mula 33 While the majority of observed spreads is rather small we find peaks atround figures (01 02 10) The peaks are not easily explained from an economicperspective assuming rational agents We hypothesize that this pattern stems froma human tendency for round figures serving as an anchor point on a discrete pricegrid12 While a deeper analysis of this phenomenon and its compatibility with theefficient market hypothesis is certainly an interesting research topic on its own it isnot the focus of this study and shall not be further discussed We are interested in the

Notes This figure shows the empirical distribution of the bid-ask spread (left) and the relative bid-askspread (right)

FIGURE 33 Sample Distribution of Stock Spreads

general interplay between analyst coverage the bid-ask spread and stock liquidity

11We find that the American cloud-based software company SALESFORCECOM to have the high-est analyst coverage overall (45 analysts) ADIDAS the German manufacturer of sports goods hasthe highest coverage across all EU countries (39 analysts)

12The occurrence of peaks could also be amplified by the definition of a minimum tick size for certainilliquid stocks equal to the level of the peak In the recent past there has been a race to the bottomregarding the price granularity however indicating that this explanation can not be the sole reason forour observation


27

We proxy the illiquidity of a stock by computing the illiquidity measure proposedby Amihud (2002) based on Formula 31 The average relative bid-ask spread perstock in our sample is plotted against the average number of analysts in Figure 34revealing a negative link A similar pattern arises between the illiquidity measureand analyst coverage indicating that highly covered stocks have a lower bid-askspread and are more liquid than stocks with low analyst coverage13 In summary

Notes This figure shows a plot of the relative bid-ask spread against analyst coverage (left) and a plotof the Amihud (2002) illiquidity measure against analyst coverage (right)

FIGURE 34 Analyst Coverage and the Relative Bid-Ask spreadthe Amihud(2002) Illiquidity Measure

our descriptive analysis shows that there seems to be a link between analyst cover-age and market size illiquidity and the bid-ask spread Especially market capitaliza-tion seems to be highly correlated with analyst coverage which makes it mandatoryto control for this factor in our empirical analysisFinally we compute the idiosyncratic risk measure Ψi for each stock in our samplein a three-step procedure First we compute the daily market return as the marketvalue-weighted return of all stocks included in the Thomson Reuters Global EquityIndex Second we compute the R2

i for each stock i in our sample by regressing themarket model for each stock in our sample based on Equation 35 As R2

i gives ameasure for the variation in stock return explained by variation in market returns1minus R2

i describes the idiosyncratic risk Following the literature we compute Ψi by

13Note that we can not derive any causal relation from these findings Further we do not control formarket size which we have shown to be correlated with analyst coverage as well We find a positivecorrelation of 035 between the relative bid-ask spread and illiquidity in our data


28

transforming the idiosyncratic risk

Ψi = ln

(1minus R2

i

R2i

)(37)

Figure 35 shows the sample distribution of Ψi As we are interested in changes to Ψi

associated with the introduction of MiFID II we compute Ψi for different sub peri-ods before and after MiFID II Note that a value of Ψi = 0 indicates the idiosyncraticrisk of a stock to be responsible for 50 of the total stock return variation

Notes This figure shows the empirical distribution of the idiosyncratic risk measure Ψ where Ψi =ln((1minus R2

i )R2i ) R2

i denotes the determination coefficient from regressing the stock return of stocki on the market return ie R2

i gives the return variation explained by the market model commonlyreferred to as the systematic risk component of stock i Note that a hypothetical value of R2

j = 50

implies Ψj = 0

FIGURE 35 Empirical Distribution of the Idiosyncratic Risk Measure Ψ

36 Empirical Analysis

Our empirical analysis is structured as follows First we compute the difference-in-differences estimator for five market shaping factors (analyst coverage illiquiditytrading volume bid-ask spread and idiosyncratic risk) to estimate the effect of Mi-FID II on these factors Second we analyze the impact of analyst coverage on thebid-ask spread and idiosyncratic risk by employing a fixed effects regression modelWe include interaction terms in our regression to test whether MiFID II has an ob-servable impact on the relationship between analyst coverage information asymme-try and idiosyncratic risk


29

361 Difference-in-Differences Approach

Using the following regression model we estimate the difference-in-differences es-timator for our variables of interest

yi = α + βtreati + γa f teri + τ(treati times a f teri) + ui (38)

where yi is the measurement value of the dependent variable for stock i treati indi-cates whether company i is based in the EU or the US We identify the jurisdictionin which stock i is listed based on the ISIN a f teri indicates whether the observationstems from before or after the implementation of MiFID II Consequently τ resem-bles the difference-in-differences estimator We use daily data from October 2017November 2017 February 2018 and March 2018 surrounding the implementation ofMiFID II on 3rd of January 2018 We choose the respective time frames consideringthat by increasing the estimation window we face a trade-off between measuringa persisting long-term effect and the risk of measuring the effect of confoundingevents Our sample consists of 1281 listed companies that are affected by MiFID II(treatment group) and 1641 US-based companies (control group) during the sametime window (see Table 32) Table 33 shows the estimation results for the meantreatment effect τ of the implementation of MiFID II on European stocks We furthercompute the difference-in-differences estimator for three different subgroups basedon quantiles for analyst coverage in order to study whether MiFID II impacts stockswith low or high coverage differently We find our results not to be largely driven bya specific subgroup Contrary to our expectations the number of analysts coveringa stock did not significantly decrease due to the implementation of MiFID II14 Wefind the largest decrease of analyst coverage by -041 analysts per stock across stockswith a high coverage but this effect is not statistically significant We derive that themere consumption of research did not change significantly with the introduction ofMiFID II in the short runWe observe that MiFID II led to a small decrease in overall stock liquidity As ex-pected and motivated in Section 32 this effect is most eminent for stocks with lowcoverage While observable in both Panels (Table 33) the effect seems to declineover time We further find a decrease in trading volume which is significant at the1 level for stocks with low coverage across both time windowsObserving the bid-ask spread we find a significant effect across all subgroups andtime frames except for highly covered stocks in the six months time frame Thiseffect does not seem to be caused by a change in illiquidity as we find an effect ofMiFID II on the bid-ask spread of almost the same magnitude when we specificallycontrol for changes in illiquidity Investigating the surprising unambiguousness of

14The difference-in-difference approach is especially useful to determine short-term effects In Table36 we show that the research coverage decreased substantially in EU markets since the implementa-tion of MiFID II


30

this finding we hypothesize that changes to the minimum tick size which were alsointroduced with MiFID II might drive this effect Article 49 of MiFID II introduces anew tick size regime which defines the minimum tick sizes for stocks based on theaverage daily number of transactions and the stock price with the aim of not con-straining spreads by a too large tick size It is important to note that this adjustmentonly affects the granularity of the pricing grid on which limit and market orderscan be placed on To our knowledge there is no reason to believe that this changeleads to a change of the average bid-ask spread The French financial supervisionAutoriteacute des marcheacutes financiers (AMF) issued a report shortly after the implementa-tion of MiFID II stating that an increase in stock spreads can be observed which theystate can be traced back to changes in the minimum tick size of affected stocks (AMF2018)15 While our results confirm this observation it also highlights the importanceof controlling for MiFID II in our subsequent multivariate regression model An-other cause of an increase in bid-ask spreads which should not be overlooked is apotential change in the behavior of investors They might be more reserved in shar-ing information gathered from research reports that they consciously paid forComputing the difference-in-differences estimator for the measure of the stock spe-cific idiosyncratic risk (Ψi) we find that the idiosyncratic risk increased significantlysince the implementation of MiFID II16 The effect is highly significant across all sub-samples with an increase in the average Ψi ranging from 143 to 322 This changeis quite substantial considering that the average value for Ψi for EU stocks beforeMiFID II is 219 in our sample

362 Multiple Regression on the Bid-Ask Spread

Next we want to study the relationship between analyst coverage and informationasymmetry We do so by estimating the following baseline regression model

log(Spread)it = β0 + β1log(Analyst Coverage)it + β2 Illiquidityit

+ β3log(Market Capitalization)it

+ β4MiFID II times log(Analyst Coverage)it + ci + uit (39)

log(Spread)it describes the logarithmic form of the bid-ask spread of stock i at theend of trading day t and serves as proxy for the level of information asymmetry Ourmain interest lies in the coefficient of log(Analyst Coverage) which yields insightinto the influence of analyst coverage on the asymmetric information component of

15Consulting the European Securities and Markets Authority (ESMA) Deutsche Boumlrse Group alsostates that they find an increase in spreads for equities traded at Frankfurter Wertpapierboumlrse afterMiFID II (ESMA 2018)

16We use an estimation period of one month of daily trading data to compute Ψi before and afterMiFID II


31

the bid-ask spread17 Note that we include both the bid-ask spread and the ana-lyst coverage in their logarithmic form18 This procedure allows us to estimate theelasticity of the bid-ask spread with respect to analyst coverage We include the illiq-uidity measure proposed by Amihud (2002) to capture changes in the bid-ask spreadcaused by an increase in inventory holding costs An increase in the illiquidity mea-sure should ceteris paribus lead to an increase in the bid-ask spread as it increasesthe inventory holding costs of a stock As shown in Section 351 analyst coverageis highly dependent on the market capitalization of a company To account for thissize effect we include the market value of a company as an explanatory variable inour regression model Note that the error term ci captures time-constant order pro-cessing costs Using fixed effects allows us to completely get rid of ci MiFID II rep-resents a dummy variable indicating whether the observation is affected by MiFIDII ie MiFID II equals 1 if the observation stems from an EU stock in the year 2018and 0 otherwise We include MiFID II for two distinct reasons First we only get anunbiased estimator for the coefficient of our interaction term between MiFID II andlog(Analyst Coverage) by including both variables in their primal form and secondwe are thereby able to capture the effect of MiFID II on the bid-ask spread Addi-tionally we control for year and country fixed effects Year 2018 indicates whetherthe observation stems from the calendar year 2017 or 2018 We include this vari-able in order to control for seasonal changes in the level of information asymmetrywhich else would be falsely attributed towards the implementation of MiFID II Ourregression results are shown in Table 34We find that an increase in analyst coverage leads to a statistically significant de-crease in the bid-ask spread The effect is also significant at the 1 level in all sub-groups Across our sample an increase in the analyst coverage by 10 decreasesthe average bid-ask spread by 26 indicating that the observed effect is also ofeconomic relevance This finding supports our hypothesis of an increase in analystsreducing the costs of information leading to a more efficient distribution of privateinformation The interaction term between MiFIDII and log(Analyst Coverage) isalso highly significant This finding shows that the influence of analyst coverage onthe information asymmetry component of the bid-ask spread significantly increasedwith the implementation of MiFID II As opposed to medium and highly coveredfirms we find that the introduction of MiFID II reduces the impact of an increasein analyst coverage on the bid-ask spread for companies with low coverage indi-cating that MiFID II impacts companies with low coverage in a different way Ourresult could be explained by an increased competition between investment researchanalysts having to proof the economic value of their work or analysts focusing their

17Variation in log(Spread)it effectively proxies variation in the level of information asymmetry Wecontrol for illiquidity effects regarding the bid-ask spread by including Illiquidityit in our regressionmodel

18We follow Easley OrsquoHara and Paperman (1998) by using the logarithmized value of analyst cov-erage


32

resources on producing high-quality research about companies with high coverageAs expected we find an increase in illiquidity to be associated with a significant in-crease in the bid-ask spread We find this effect across all subgroups to be of equalmagnitude Examining the economic significance an increase in the illiquidity mea-sure by 01 (which equals approximately one standard deviation) increases the aver-age bid-ask spread by a mere 017With an R2 ranging from 283 to 310 the explanatory power of our regressionresults is rather high Further many estimated coefficients show the expected signboosting the confidence regarding the validity of our proposed model specification

363 Multiple Regression on Idiosyncratic Risk

We estimate Ψit on a monthly basis for each stock in our regression model by firstregressing the return of stock i at trading day t on the market return on trading day tfor the months surrounding the implementation of MiFID II October 2017 Novem-ber 2017 February 2018 and March 2018 We compute the market return as themarket value weighted average return of stocks included in the Thomson ReutersGlobal Equity Index as described in Section 351 We use the resulting R2

imonth tocompute Ψit for each stock in our sample In a second step we study the impact ofanalyst coverage on Ψit

Ψit = β0 + β1log(Analyst Coverage)it + β2 Illiquidityit


+ β4MiFID IIit times log(Analyst Coverage)it + ci + uit

(310)

While the coefficient of log(Analyst Coverage)it does not have a straightforward eco-nomic interpretation we can derive a statement by observing its sign An increase inanalyst coverage leads to a significant decrease in the amount of idiosyncratic risk ofa stock indicating that more analysts covering a stock lead to a higher level of syn-chronicity between stock market returns and individual stock returns This findingis consistent with the results of Piotroski and Roulstone (2004) who find a positivelink between analyst activities and stock price synchronicity as well Piotroski andRoulstone (2004) attribute their findings to analysts increasing the informativenessof prices through intra-industry information transfersWe further find higher illiquidity to be associated with a higher level of idiosyncraticrisk A stock that is traded less frequently might be more separated from the generalmarket development Moreover the information transfer from informed traders viatrading and the ability of uninformed traders to learn about the intrinsic value ofa stock from price movements might be limited if less trading takes place Inter-estingly this effect can not be observed for highly covered stocks suggesting a linkbetween analyst coverage and illiquidity This effect would be consistent with the


33

consideration of analysts functioning as a company promoter making a stock morepopular across investors by issuing company specific researchRegarding the coefficient of the interaction term MiFID II times log(AnalystCoverage)we find that the magnitude of the impact of analyst coverage on idiosyncratic riskincreases with the implementation of MiFID II This effect seems to be most apparentfor highly covered stocks and is not observable for stocks exhibiting low to mediumlevels of analyst coverage We also find that market capitalization significantly de-creases idiosyncratic risk This effect could be explained by small companies beingmore specialized in the products or services they offer while large conglomeratestend to possess a more diversified portfolio and operate in many different indus-tries at the same time Subsequently general market variation might be better inexplaining returns of larger companies than smaller companies The coefficient ofMiFID II provides further evidence supporting our findings from our difference-in-differences analysis We observe a significant increase in the overall idiosyncraticrisk caused by the implementation of MiFID II As discussed in Section 343 an in-crease in the idiosyncratic risk can either be interpreted as an increase or a decreasein information asymmetry with two different strains in the existing literature sup-porting either the first or the latter view For this reason we analyze the behaviorof the bid-ask spread based asymmetric information measure and idiosyncratic riskseparately While we do not directly try to assess the link between idiosyncratic riskand information asymmetry our results suggest that a lower level of idiosyncraticrisk can be associated with a lower level of information asymmetry We base ourview on the regression results shown in Table 34 and Table 35 An increase in ana-lyst coverage seems to decrease both information asymmetry as well as idiosyncraticriskOur results also provide empirical evidence for a more fundamental question Whatis the role of an analyst in financial markets We show that financial analysts playan important role in disseminating information We further find evidence whichsupports the theoretical model proposed by Grossman and Stiglitz (1980) in whichprivate information can only be observed by investors if a specific price is paid Weargue that an increase in the number of analysts increases the competition betweenanalysts which in turn leads to a decrease in the price for private information makingit easier for investors to access the information This reduces the discrepancy of theinformation level between market participants We observe the resulting decrease inasymmetric information costs by a reduction in the bid-ask spread

364 Summary Statistics on Medium-Term Effects on Research Coverage

First explorative studies begin to emerge considering the medium-term effects ofMiFID II on research coverage eg the 11th Annual IR Survey conducted by thecompany CITIGATE DEWE ROGERSON finds that sell-side analyst research declined


34

for 52 of UK based companies and 39 for EU companies excluding UK (CitigateDewe Rogerson 2019)While our study focuses on short-term effects surrounding the implementation ofMiFID II ndash not least to avoid attributing effects of confounding events to the effect ofMiFID II ndash we provide descriptive statistics on the general development of the mar-ket for investment research following the implementation of MiFID II (Table 36)On a year-on-year basis we find that the overall coverage decreased from a total of12353 available earnings forecasts for 1271 companies at the beginning of 2018 to11387 available earnings forecasts for the same companies in 2019 which amountsto an overall decrease in coverage by 782 since implementation of MiFID II More-over 323 of all companies and 591 of companies with already low coverage(25-Quantile) in 2018 completely lost coverage However it is tough to link thisdecline in coverage solely to the impact on MiFID II without further analysis Inaddition variation of analyst coverage seems to be low for companies with low cov-erage as the coverage did not change at all for 4190 of companies which alreadyhad low coverage in 2018

37 Conclusion

How analysts affect stock markets is of widespread interest and has been widelydiscussed in the literature We add to the literature by studying the impact of aregulatory change on analyst behavior With the introduction of MiFID II researchservices conducted by financial analysts have to be paid for explicitly for the firsttime creating a unique opportunity to study the role of financial analysts We hy-pothesize that analysts face enhanced competition which should have a measurableeffect on the bid-ask spread of a covered stock Using a difference-in-differences ap-proach we first show that MiFID II had a substantial impact on European financialmarkets We show that MiFID II led to an increase in the overall bid-ask spread aswell as the stock specific idiosyncratic riskWe further show that an increase in analyst coverage reduces the bid-ask spread of astock Our results are robust when we control for factors potentially influencing thebid-ask spread By controlling for liquidity we capture the variation of the bid-askspread caused by a change in the implicit costs of asymmetric information and findanalysts to reduce asymmetric information We find that this effect is amplified byMiFID IIWith a total of 2927 observed firms we are confident that our results can be gener-alized to a certain level Nevertheless more studies have to be conducted focusingon long-term and industry specific effects to understand the full impact of MiFIDII on European financial markets Another promising chance to study the effect ofa regulatory change affecting analyst behavior in the future will certainly arise as


35

SEC has to decide on how to proceed after their 30 month non-action relief issued inOctober 2017 concerning US research providers


36

TABLE 31 Summary Statistics by Country

Notes This table shows summary statistics for four months of daily data surrounding the implemen-tation of MiFID II (October 2017 November 2017 February 2018 and March 2018) Analyst Coverageis proxied by the number of available earnings-per-share estimates per firm in the IBES database

Country Firm-Day Observations Analyst CoverageFreq Percent Cum Min Max Mean

European CountriesAT 2175 087 087 2 19 719BE 3480 140 227 1 31 658CZ 174 007 234 2 11 609DE 13875 557 792 1 40 1249DK 3247 130 922 1 32 1060ES 4894 197 1119 1 34 1415FI 5216 210 1328 1 28 804FR 14470 581 1909 1 32 1010GB 22322 897 2806 1 33 1214GR 1698 068 2874 1 17 601HU 348 014 2888 3 9 633IE 2926 118 3006 3 31 1364IT 7596 305 3311 1 29 755LU 1467 059 3370 2 25 1104MT 261 010 3380 1 5 267NL 5112 205 3586 1 31 1149PL 5099 205 3791 1 17 522PT 1131 045 3836 2 24 984SE 14525 583 4419 1 30 556

United States of AmericaUS 138920 5581 10000 1 45 1090

Total 248936 10000


37

TABLE 32 Summary Statistics for Analyst Coverage

Notes This table shows summary statistics about analyst coverage derived from four months of dailydata surrounding the implementation of MiFID II (October 2017 November 2017 February 2018 andMarch 2018) for 1646 US and 1281 EU firms

Jurisdiction EU USMean Std Dev Mean Std Dev

Analyst Coverage () 981 738 1090 779Quantile 1 ndash LOW 317 144 361 147Quantile 2 ndash MEDIUM 838 212 851 222Quantile 3 ndash HIGH 1899 491 1976 630

Firms () 1281 1646Firm-Day Observations () 110016 138920

TABLE 33 The Effect of MiFID II on European Financial Markets

Notes This table shows the difference-in-differences estimator for multiple variables of interest sur-rounding the implementation of MiFID II The control group consists of 1646 US firms (not affectedby MiFID II) and the treatment group consists of 1281 EU firms (affected by MiFID II) Illiquidity ismeasured by the illiquidity measure proposed by Amihud (2002) The estimator is multiplied by 106The estimator for the relative bid-ask spread is multiplied by 100 for readability

Analyst Coverage (ALL) (LOW) (MEDIUM) (HIGH)Mean of

Diff-in-Diffs(Treatmentsvs Controls)

t-statistic

Mean ofDiff-in-Diffs(Treatmentsvs Controls)

t-statistic


t-statistic


t-statistic

Panel A Four Months (Mminus2 vs M+2)

Analyst Coverage minus003 minus007 016 120 003 015 minus009 minus017

Illiquidity 001lowastlowastlowast 424 003lowastlowastlowast 339 001lowast 187 000 009

log(Trading Volume) minus029lowastlowastlowast minus305 minus038lowastlowastlowast minus258 minus020 minus150 minus009 minus071

rel Bid-Ask Spread 007lowastlowastlowast 529 009lowastlowastlowast 316 008lowastlowastlowast 401 003lowastlowastlowast 345

rel Bid-Ask Spread(1) 006lowastlowastlowast 521 009lowastlowastlowast 319 007lowastlowastlowast 365 003lowastlowastlowast 365

Idiosyncratic Risk (Ψ) 170lowastlowastlowast 1682 165lowastlowastlowast 924 145lowastlowastlowast 840 193lowastlowastlowast 1142

Panel B Six Months (Mminus3 vs M+3)

Analyst Coverage minus034 minus084 minus010 minus071 minus018 minus085 minus041 minus077

Illiquidity 001lowastlowast 241 001lowast 170 000 090 000 147

log(Trading Volume) minus035lowastlowastlowast minus368 minus059lowastlowastlowast minus390 minus015 minus113 minus021lowast minus174

rel Bid-Ask Spread 003lowastlowast 250 006lowastlowast 206 004lowastlowast 206 000 minus001

rel Bid-Ask Spread(1) 003lowastlowast 227 006lowastlowast 208 minus004lowast 192 000 minus043

Idiosyncratic Risk (Ψ) 153lowastlowastlowast 1409 322lowastlowastlowast 3298 143lowastlowastlowast 766 185lowastlowastlowast 1036lowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001 (1)Including a control variable for liquidity


38

TABLE 34 The Impact of Analyst Coverage on Information Asymmetry

Notes This table shows fixed-effects regression results on log(Spread) for the whole sample (ALL)and three sub-samples based on quantiles of analyst coverage (LOW MEDIUM HIGH) We measureIlliquidity by computing the illiquidity measure proposed by Amihud (2002) Market Capitalizationis measured in US Dollar

log(Spread)Analyst Coverage (ALL) (LOW) (MEDIUM) (HIGH)

Variables of Interest

log(Analyst Coverage) minus026lowastlowastlowast minus012lowastlowastlowast minus025lowastlowastlowast minus042lowastlowastlowast

(minus6510) (minus1056) (minus1248) (minus2262)

Illiquidity 170lowastlowastlowast 192lowastlowastlowast 163lowastlowastlowast 222lowastlowastlowast

(3791) (3612) (1402) (805)

Market Capitalization 000lowastlowastlowast minus000 minus000lowastlowastlowast 000lowastlowastlowast

(1026) (minus103) (minus2388) (2127)

MiFID IItimeslog(Analyst Coverage) minus003lowastlowastlowast 008lowastlowastlowast minus014lowastlowastlowast minus116lowastlowastlowast

(minus337) (383) (minus302) (minus2634)

Control Variables

MiFID II 002 minus007lowastlowast 032lowastlowastlowast 322lowastlowastlowast

(128) (minus232) (329) (2498)

Year [2017=02018=1] 006lowastlowastlowast 005lowastlowastlowast 005lowastlowastlowast 008lowastlowastlowast

(823) (381) (437) (655)

Country [US=0EU=1] 165lowastlowastlowast 156lowastlowastlowast 168lowastlowastlowast 171lowastlowastlowast

(21319) (11015) (12855) (13415)

Constant minus350lowastlowastlowast minus364lowastlowastlowast minus339lowastlowastlowast minus311lowastlowastlowast


Observations 241598 80558 80277 80763R2 310 283 309 298

t statistics in parentheseslowast p lt 010 lowastlowast p lt 005 lowastlowastlowast p lt 001


39

TABLE 35 The Impact of Analyst Coverage on Idiosyncratic Risk

Notes This table shows fixed-effects regression results on the idiosyncratic risk measure Ψ for thewhole sample (ALL) and three sub-samples based on quantiles of analyst coverage (LOW MEDIUMHIGH) We measure Illiquidity by computing the illiquidity measure proposed by Amihud (2002)Market Capitalization is measured in US Dollar

Relative Idiosyncratic Risk (Ψimonth)Analyst Coverage (ALL) (LOW) (MEDIUM) (HIGH)




Illiquidity 112lowastlowastlowast 092lowastlowastlowast 097lowastlowastlowast minus059(1671) (1169) (553) (minus143)

Market Capitalization minus000lowastlowastlowast minus000lowastlowastlowast minus000lowastlowastlowast minus000lowastlowastlowast


MiFID IItimeslog(Analyst Coverage) minus015lowastlowastlowast 003 009 minus053lowastlowastlowast

(minus1289) (099) (135) (minus820)

Control Variables

MiFID II 190lowastlowastlowast 139lowastlowastlowast 124lowastlowastlowast 343lowastlowastlowast

(6799) (3127) (852) (1792)

Year [2017=02018=1] minus225lowastlowastlowast minus193lowastlowastlowast minus214lowastlowastlowast minus264lowastlowastlowast


Country [US=0EU=1] 010lowastlowastlowast 028lowastlowastlowast 022lowastlowastlowast minus018lowastlowastlowast

(864) (1333) (1132) (969)

Constant 358lowastlowastlowast 339lowastlowastlowast 377lowastlowastlowast 407lowastlowastlowast

(25037) (13739) (5984) (5036)

Observations 242170 80742 80578 80850R2 215 162 206 264



40

TABLE 36 Medium-Term Development of Analyst Coverage since the Introduc-tion of MiFID II (2018 ndash 2019)

Notes This table shows the change in analyst coverage between March 2018 to March 2019 for EUstocks affected by MiFID II We measure the change in analyst coverage be computing the change inthe amount of available earnings-per-share estimates available in the IBES If the drop in coverageis based on the delisting of the respective stock we drop the observation from our sample

All Firms Low CoverageN = 1 271 N = 389 (Q025)

∆Coverage (2018rarr 2019) Total Total

ge +3 72 566 12 308+2 86 677 26 668+1 155 1220 74 19020 311 2447 163 4190minus1 275 2163 77 1979minus2 139 1093 13 334le minus3 192 1511 1 026No more Coverage 41 323 23 591

Total 1271 10000 389 10000

41

Chapter 4

Innovative Finanzierungen uumlberInitial Coin Offerings Strukturund bisherige Performance

Chapter 4 has been published as a journal articleHaumlfner David and Dirk Schiereck (2020) Innovative Finanzierungen uumlber InitialCoin Offerings Struktur und bisherige Performance in Zeitschrift fuumlr KMU undEntrepreneurship (ZfKE) 682 pp 73-98 ISSN 1860-4633DOI httpsdoiorg103790zfke68273

42

Chapter 5

Statistical Properties ofCryptocurrency Order Books

51 Introduction

Securities exchanges or multilateral trading platforms allow us to analyze the be-havior of interacting market participants In these financial markets all market par-ticipants trade the same asset and pursue the same goal (profit) which should in-centivize each market participant to act rational This setting allows to study notonly general economic and financial theories but also theories of individual humanbehavior By analyzing market microstructure data apparently universal laws havebeen discovered in the literature that seem to hold true independent of the tradedasset or the exchange (eg Bouchaud Meacutezard and Potters 2002 and Naeligs and Skjel-torp 2006) These statistical laws help to better describe the competitive behaviorof market participants and some of these laws have revealed noticeable similaritiesacross different assets time periods and regions (Potters and Bouchaud 2003 Cont2001 and Mantegna and Stanley 1999)In this study we investigate whether properties in established securities markets canalso be found in cryptocurrency markets Cryptocurrencies did not yet exist whensome of the statistical laws were first discovered and verifying these properties incryptocurrency markets would strongly boost their validity and the robustness ofthose characteristics adding important evidence to the existing microstructure liter-atureGroundbreaking work has been done by OrsquoHara (1997) who analzyes the devel-opment of microstructure theory and the evolution of the literature to that pointand more recently the impact of high-frequency trading on market microstructure(OrsquoHara 2015)Studying market microstructure adds to a deeper understanding of financial mar-kets in general and the real economy benefits from efficient markets in many wayseg risk sharing and an efficient capital allocation As market microstructure andthe market design directly affect market efficiency inefficient markets on the microlevel lead to higher transaction costs and imply that someone can earn money on

Chapter 5 Statistical Properties of Cryptocurrency Order Books 43

someone elses expense If a market is inefficient on a microscopic level direct andindirect transaction costs will inevitably increase Further an ideal price discoverymay be disturbed which can lead to gaps between the price and the perceived valueof an asset This again affects the real economy as an investor is only willing to holdan asset if he is confident about its value This is crucial for companies to be ableto use financial markets as a reliable source of capital These relations between themarket microstructure and the real economy are especially relevant for emergingcryptocurrency markets Recent developments in the acceptance of cryptocurren-cies as a source of capital for companies ndash eg via Initial Coin Offerings (ICOs) ndash orfor portfolio diversification makes analyzing cryptocurrencies on a micro level moreimportant than ever beforeThe remainder of this study is structured as follows In Section 52 and Section 53we first describe the operating principle of a Limit Order Book (LOB) and give abrief overview of the cryptocurrencies examined in this study Further a detaileddescription of the data collection process is provided and the data set used for thesubsequent empirical analysis is described and descriptive statistics are presentedIn Section 54 we reconstruct the limit order books of three different cryptocurren-cies from our data We compute the aggregated limit order book volume also re-ferred to as the slope of the order book As shown in our descriptive analysis wefind empirical evidence for volume peaks in the LOB at specific price levels relativeto the best price We hypothesize that these peaks do not appear at random but arerather caused by ldquolazyrdquo investors disregarding the granularity of the price grid Wetest the empirical significance of our observation and find these peaks to be statisti-cally highly significant and to appear across all examined LOBsWe further find that the slope of the LOB varies over time ie the aggregated LOBchanges its shape This finding raises the question whether information is hiddenin the slope of the order book We examine the explanatory power of the slope inSection 55 Similar to Naeligs and Skjeltorp (2006) who study stock LOBs we inves-tigate three groups of models using the slope of the order book to explain changesin prices trading volume and the correlation between price changes and tradingvolume Our results suggest that the slope of the LOB can explain changes in the de-pendent variables We further find evidence suggesting that limit orders placed faraway from the best price still carry price-relevant information Section 56 concludesand discusses implications of our findings

52 Structure of a Limit Order Book

An asset can only be traded when a buyer and a seller find to each other Whilethe buyer wants to pay as little as possible for the respective asset the sellers wantssell for the highest possible price Exchanges serve as a platform to enable trading


by helping potential buyers and sellers to find a trading partner Since the devel-opment of stock exchanges two alternative systems have emerged differing in theway of how trading is organized In quote-driven markets market makers will postbuy and sell quotes for which they are willing to trade an asset1 A market makeris responsible to provide the market with liquidity by matching incoming ordersagainst other orders or by buying or selling from his own inventory While marketmakers provide liquidity ndash which is beneficial to all market participants ndash quote-driven markets lack transparency as market participants do not know the identityof their counterpart2 The second way to organize trading on an exchange is via alimit order book Exchanges operating via a LOB are commonly referred to as order-driven markets In an order-driven market all valid buy and sell orders are listedin the (electronic) LOB which each market participant has access to providing fulltransparency to all market participants Order driven markets became increasinglypopular in the past and many of the largest stock exchanges currently operate withLOBs (eg NYSE Euronext Deutsche Boumlrse Nasdaq the Tokyo Stock Exchange andthe London Stock Exchange) The matchmaking of a LOB is based on a set of pre-defined mechanical rules and strictly follows a first-come first serve principle andmeets a price-time priority Further the order processing mechanics of LOBs is au-tomated and follows transparent rules On the contrary the flow of incoming ordersis solely based on traders internal decision making processesFrom a scientific point of view the arrival of limit orders in an order-driven marketis a complex field of study as the order placement is not bound to any rules andpurely stems from the decision of an individual to indicate the willingness to buyor sell a specific quantity of the respective asset during a specific point in time for aself-set price Thus studying limit order books yields the most detailed insight intodynamic behavior in financial markets As every decision by a market participant isdirectly linked to a potential financial loss or gain market participants are stronglyincentivized to act rational providing an ideal experimental setting to study eco-nomic behaviorFigure 51 depicts a stylized version of a LOB Each block on the demand side re-

sembles one unit of the traded asset For simplicity we assume that each limit orderrefers to exactly one unit of the traded asset We define the best bid b(t) as the high-est price level at which at least one market participant is willing to buy one unit ofthe asset a(t) describes the lowest price for which at least one market participantis willing to sell In this steady state of the order book no trade would occur as

1While cryptocurrency markets are not order-driven and there are no market makers in Bitcoinmarkets (Marshall Nguyen and Visaltanachoti 2019) Holste and Gallus (2019) find empirical evi-dence for market maker quotes at cryptocurrency exchanges Market maker type of traders issuelimit orders with attractive prices however the volume of their offers is rather small and they shouldtherefore not be regarded as liquidity providers

2Anonymity is no distinct feature of quote-driven markets When trading is organized by operatinga limit order book the identity of the counterpart if often unknown to traders and only referred to bya unique number Solely the exchange operator knows about the true identity of the buyer and seller


-

-

-

-

-

-

-

-

-

-

-

MinimumTick Size

Demand(Bid)

Supply(Ask)

Bid-AskSpread

b(t)

a(t)

Price

FIGURE 51 Structure of a Limit Order Book Own Representation based on Preiset al (2006)

the lowest sell price is higher than the highest ask price Consequently a(t)minus b(t)describes the implicit bid-ask spread in this modelObserving the state of the LOB shown in Figure 51 imagine the case that a new mar-ket buy order arrives comprised of two units at a price level of a(t) + 1 This marketorder would be matched against the best limit sell order available in the order bookIn the current state of the order book there exists exactly one unit at a price level ofa(t) and two units at a price level of a(t) + 1 Thus the unit at price level a(t) will bematched against the incoming order The next best price is at a(t) + 1 However asthere are two units available it is not clear which one should be matched against theremaining unit from the market order In this case a time priority is met ie the limitsell order at price a(t) + 1 which has been submitted first will be matched againstthe market order Ceteris paribus this would lead to an increase in the bid-ask spreadby one tick as there is no volume left at a(t) after the market order has been matchedOne would describe the arrival of a market sell order in an analogous mannerIf a limit order arrives at a price level which can not directly be matched it will beadded to the LOB This scenario is shown for a limit buy order with a size of one unitat a price level of b(t)minus 2 in Figure 51 As there exist higher bids in the order bookthis order will not be executed immediately but rather added to the order book (in-dicated by the arrow) and remain in the order book until either all higher bid ordersare already matched or canceled and a sell order at price level b(t)minus 2 arrives or theorder is canceled by the trader who submitted the orderIt is important to note that besides the arrival of the market buy order or the arrival


of the limit buy order all subsequent processes are based on predefined mechani-cal rules The order book follows two simple principles to decide how an order ismatched price priority and time priority with the first dominating the latter Dueto the seemingly nondeterministic arrival of orders the volume at each price levelin the order book is nondeterministic as well and provides an interesting researchfield on its own Recent studies also discuss the endogeneity of the size of the bid-ask spread As shown above the bid-ask spread changes based on the arrival ofnew orders and is thus based on the behavior of the market participants Howeverwhen the size of the bid-ask spread almost always equals the minimum tick size ndasheg when the traded asset is very liquid ndash the question arises whether the bid-askspread is in fact perceived as exogenous rather than endogenous by market partici-pants (Biais Hillion and Spatt 1995) In this case the granularity of the price grid ndashwhich is set by the minimum tick size ndash may prevent the observability of the endo-geneity of the size of the bid-ask spreadWhile determining the price level of a limit order market participants have to taketwo antagonizing criteria into account Opting to place a limit order close to or atthe bid-ask spread leads first to a higher probability that the order will be fulfilledand secondly a decrease in the mean time passing until the order will be matchedOn the other hand placing a limit order close to the bid ask-spread yields the risk ofhaving the limit order executed at an unfavorable price abandoning the chance oftaking advantage of price movements towards the desired direction (eg the possi-bility to sell at a higher price or buy at a lower price) Thus every market participanthas to select a price level for his limit order which takes into account his individualprice and time preferences

53 Data Collection and Description

In order to analyze order book characteristics in cryptocurrency markets we col-lect trading data from one of the largest US-based cryptocurrency exchanges Theexchange generates a turnover of 1 bn USD and offers one of the largest onlinetrading platforms for cryptocurrencies to a peak of more than 10 mn worldwideusers At the time of data collection the cryptocrurrencies Bitcoin (BTC) BitcoinCash (BCH) Ethereum (ETH) and Litecoin (LTC) could be traded against each otherand against US Dollar (USD) and Euro (EUR)Bitcoin is a peer-to-peer electronic cash system first proposed by Nakamoto (2008)The main advantage of Bitcoin is the redundancy of intermediaries as transactionscan be made peer-to-peer and are tamper-proof as all transactions are recorded andstored in a blockchain Bitcoins can either be created via a process known as min-ing or purchased on an exchange While a traditional money transfer typically in-volves a third party (eg a bank) no such entity is necessary to transfer Bitcoin


In order to send Bitcoin from one party to another only a bitcoin wallet and an in-ternet connection is necessary In the Bitcoin network many miners define theexact structure of Bitcoin using an algorithm thus the currency is not controlled byone single authority This organizational structure leads to continuous majority de-cisions where computational power is used to vote Bitcoin is transparent as eachtransaction is stored and traceable in the blockchain A new transaction is verifiedonly if the majority of miners in the system confirms the transaction Although thetransaction history is visible the counterparties of a transaction remain anonymousas solely the address of the Bitcoin wallet of the sending and receiving entity canbe observed With approximately ten minutes the average transaction time in theBitcoin network is many times faster than a traditional bank transaction which stilltakes some business days to be completed However Bitcoin is currently not able tocompete with the speed of large credit card operatorsAs of September 2019 Bitcoin has a total market capitalization of approximately 189billion USD which is about 70 of the total cryptocurrency market The marketcapitalization constantly changes by either a change in the BTCUSD price or an in-crease in the available amount The maximum amount of Bitcoin is mathematicallylimited to 21 mn BTC of which approximately 18 mn BTC have been mined as of2019 Computing power needed to mine the remaining amount increases dynam-ically with the total computing power in the network Bitcoin has many possibleapplications as it can be used as means of payment a protection against inflation oras a value storage With a maximum of 21 mn BTC its limited availability makesBTC a scarce resource Further Bitcoin is decentralized and available through theinternet making it portable and tradeable in small units with low storage costs ndash onemajor advantage over storing values physically eg by buying goldA big problem of the Bitcoin network is the transaction speed and energy consump-tion Currently Bitcoin can only verify seven transactions per second With an in-crease in popularity more transactions need to be verified per second creating apotential bottleneck for the mainstream adoption and usability in everyday transac-tions Moreover one single Bitcoin transaction consumed at least 300 kWh in 2018(De Vries 2018 p 804) while a bank transfer by credit card only needs 0001 to 0002kWh In 2017 the cryptocurrency Bitcoin Cash (BCH) was introduced to tackle thisscaling challenge Bitcoin Cash was created by a hard fork of the Bitcoin blockchainmaking Bitcoin Cash technically almost identical to Bitcoin except for an increasedblock size of the blockchain resulting in a higher number of transactions that canbe verified per second While Bitcoin Cash is faster than Bitcoin larger blocks areharder to process favoring larger miners which is diametral to the initial Bitcoinconcept of decentralization Since its creation Bitcoin Cash developed towards anindependent cryptocurrency and is the fourth largest cryptocurrency in terms ofmarket capitalization (53 bn USD) as of September 2019


Similar to Bitcoin and Bitcoin Cash Ethereum is based on the blockchain technol-ogy However Ethereum acts as both a cryptocurrency and a decentralized comput-ing platform that allows developers to execute decentralized computer programsEthereum was introduced in 2015 by Vitalik Buterin and is used as a means of pay-ment in the Ethereum network As of September 2019 Ethereum is the secondlargest cryptocurrency after Bitcoin with a market capitalization of approximately19 bn USD From a consumer perspective Ethereum can be used to do everythingthat is possible with fiat money Ethereum can be spent invested saved or it can betransferred to peers For companies Ethereum can be used to finance MampA activi-ties and other investment decisions without a financial intermediary Neither a banknor a payment processor is needed to transfer Ethereum ie bank fees for grantinga financing loan can be avoided completely by using Ethereum On a broader scaleEthereum has the potential to make the economy more efficient and increase produc-tivity as the present value of projects increases due to lower capital costs leading tomore profitable projects overallIn the Ethereum network the currency ETH is used to pay participating computersfor providing computational power ie Ether can also be mined While the finan-cial industry is assumed to be the primary user of the blockchain concept (Noferet al 2017) the Ethereum blockchain also receives increasing attention from moredistant industry sectors lately Eg a USD 30 mn real estate property was tok-enized with blockchain in Manhattan in 2018 (Wolfson 2018) The transaction wasbased on a theoretical Two Token Waterfall model proposed by Lippiatt and Oved(2018) utilizing the Ethereum blockchain demonstrating the wide range of possibleapplications of Ethereum The model provides a structural framework to tokenizereal assets and is based on two tokens representing debt and equity classes Con-sequently both classes combined represent the total capitalization of a transactionLippiatt and Oved (2018) state that this tokenized structure can increase liquidity ofreal assets The waterfall depicted in Figure 52 represents the flow of cash in the caseof liquidation of the tokenized asset From the flow of payments it appears that debttoken holders enjoy seniority Compared to traditional debt interest payments arenot paid on a recurring basis in this model but the accrued amount is paid at the timeof liquidation This benefits the equity token holder as fewer cash requirements arenecessary and the date of sale does not have to be predetermined allowing equityholders to sell during favorable market conditions In exchange for this flexibilityequity token holders must share their excess sales profit with debt token holders ona prenegotiated split Lippiatt and Oved (2018) utilize the distributed ledger tech-nology as a decentralized clearing house that stores all financial transactions andwhere tokens represent the ownership of real assets as smart contracts3 The authorsfurther show that the return profiles of the debt and equity token imply an underly-ing present value of the asset which would create arbitrage opportunities for traders

3In their paper Lippiatt and Oved (2018) select Ethereum smart contracts


Token AReplicates Debt

Token BReplicates Equity

Total Asset Capitalization

Cash Flow Distribution at Sale Event

A Interest

A Principal

A Excess B Excess

B Principal

FIGURE 52 Waterfall Model for Tokenized Real Assets Own representation basedon Lippiatt and Oved (2018)


if the tokens are not priced correctly Lippiatt and Oved (2018) claim that the tok-enization of real assets can reduce illiquidity The most obvious increase in liquidityis due to the simplification of trading and pricing of a real asset which creates a sec-ondary market for those alternative investments In accordance with Glosten andHarris (1988) the authors decompose the bid-ask spread and show that the asym-metric information component is reduced as a consequence of the transparency ofsmart contracts Smart contracts allow to see the supply and holdings of all marketparticipants leading to more educated investment decisions and thus reducing thelikelihood of asymmetric information (Lippiatt and Oved 2018) Further clearingcosts equal almost zero through the peer-to-peer transfer on the Ethereum networkMoreover a potential investor is not tied to either provide equity or debt but cancreate his individual riskreturn profile by blending debt and equity tokens of thesame asset making an investment attractive for a broader range of investors Whiletokenized real estate transactions are still often conducted in fiat currency in the endndash mainly due to a lack of investor acceptance ndash the real estate transaction discussedabove demonstrates how the transaction process for alternative assets is changingand how Ethereum differs from Bitcoin and Bitcoin Cash in itrsquos potential useDue to the volatile nature of the cryptocurrency market it is hard to derive a mean-ingful statement about the relevance of the above mentioned cryptocurrencies How-ever we find that the market share of Bitcoin Ethereum and Bitcoin Cash combinedremains somewhat stable over time and accounts for roughly 80 of the total cryp-tocurrency market capitalization as of September 2019 Bitcoin alone has a total mar-ket share of approximately 71 (Ethereum c 7 Bitcoin Cash c 2) Includingthese three major currencies in our analysis we are convinced to capture a represen-tative picture of the cryptocurrency market4

531 Data Acquisition Process

We use real-time market data updates for orders and trades provided by a websocketfeed which can be used to reconstruct a real-time order book While the websocketfeed is publicly available connections to it are rate-limited By creating a script weare able to store all updates received by the websocket feed locally Observing thebuilt-in Sequence Number we can assure that we do not miss any updates and infact can recreate the full order book at any point in time during our period of obser-vation Table 51 provides an example excerpt of the data that we are able to recordUpdates to the order book happen when the status of an order changes Type de-fines the event that occurs and is split into the four distinct categories receivedopen match or done Whenever a new order arrives it is first received andthen open in the LOB The order which is identified by its unique Order ID willremain in the order book until either the entity who created the order cancels it or

4Based on a market capitalization of BTC 1889 bn USD ETH 192 bn USD BCH 53 bn USD asof 04 September 2019


TABLE 51 Example of events occuring in a Cryptocurrency Order Book

Notes This table shows an excerpt of the development of the BitcoinEuro order book on 18th April2018 Note that all events shown in this table occur in just 0043 seconds If a new limit order is re-ceived it will remain open in the order book until it either gets canceled by the trader or matchedagainst another order Figures have been slightly simplified for readability eg the true Order ID iscomposed of 32 digits to guarantee uniqueness Sequence increases by 1 whenever a new event oc-curs and proves that no updates in the order book were missed We further have information on thebest bid and ask during each event

Type Order Side Price Size Time Sequence Order Remaining ReasonType ID Size

open sell 662877 215918320 0 0001received limit buy 652861 0001 215918323 1

open buy 652861 215918323 2 0001done sell 663627 215918336 3 0001 canceleddone sell 664527 215918356 4 0001 canceleddone sell 663777 215918360 5 0001 canceled

received limit buy 650761 0001 215918362 6open buy 650761 215918362 7 0001

received limit buy 651211 0001 215918363 8

the order is matched against another order The Order Type defines whether anorder has been issued as a market order (market) or a limit order (limit) Sideindicates from which side an order has been issued ie if someone wants to sell(ask side) or buy (bid side) some amount of the respective cryptocurrency Sizedefines the volume of the orderWe track the evolution of the order book for every currency pair combination whichcan be traded at the time of observation allowing us to obtain an immense amountof trading data (see Table 52) We are able to store and analyze more than 60 giga-byte worth of trading data for eleven different currency pairsFurther a Rest API is available which we use to compute periodic order book snap-shots While the Rest API is more bulky and slow it allows us to gather preprocesseddata which would be unfeasible to reconstruct from the websocket feed in a timelymanner We use the Rest API to capture a snapshot of the order book every ten min-utes We use this data to examine statistical properties of the average shape of theorder book


Table 52 gives an sample overview and some meta data about the gathered trad-ing data Our observation period spans from April 2018 to August 2019 (includinggaps) Due to hardware and software restrictions (eg forced reboots operatingsystem updates server connection losses and data storage limitations) we are notable to gather 100 of the daily order flow and therefore split the observation intomultiple sessions spread across the day


Table 53 provides a breakdown of order types and cancellation rates We find thatacross all currency pairs and order types the cancellation rate is remarkably highwith the BTCUSD sell side (9836) being the lowest overall cancellation rateMoreover we find that market orders are rare ie orders are almost exclusivelyissued as limit ordersHigh cancellation rates as depicted in Table 53 indicate the existence of high-frequencytrading (HFT) While a strict definition of HFT does not exist we find that the recentdefinition of high-frequency algorithmic trading as a subset of algorithmic tradingissued by the European Securities and Markets Authority (ESMA) under the rulesof MiFID II is a useful definition According to ESMA (European Commission 2014p36) HFT is mainly characterized by

bull an infrastructure intended to minimize network latencies (eg co-location)

bull the controlling of order initiation generation routing or execution by ma-chines without human interaction

bull high message intraday rates concerning orders quotes or cancellations

ESMA further states that HFT is characterized by a high daily portfolio turnovera high order-to-trade ratio and ending the trading day at or close to a flat position(European Commission 2014 p10)HFT is discussed controversial as the net economic benefit or loss remains unclearThe real economy benefits from HFT in many ways namely a higher liquidity anincreased trading and order book volume a reduction of the bid-ask spread and abetter price formation and execution coming along with an overall improved pricequality which ultimately decreases the cost of capital However enhanced HFTbears many risks eg an overload of the trading systems due to high order cancella-tion rates an increased price volatility and the potential for market manipulation Inaddition slower traders could stop trading suspecting that high-frequency tradersuse their informational advantage against slower traders to earn a profit off of themIn Germany HFT is regulated since 2013 by the high-frequency trading bill (Hochfre-quenzhandelsgesetz) which includes some major amendments in order to preventdangers and the misuse of HFT Notably in addition to improved system controlrisk control and transparency guidelines an order-to-trade ratio has been stipulatedwhich aims to limit the amount of updates a trading system is allowed to send to-wards an exchange The definition and measurement of the order-to-trade ratio hasto be provided in the stock exchange regulations (sect26a BoumlrsG)HFT regulation has also been discussed on an European level and responsibilities offirms engaging in HFT have also been defined in MiFID II to ensure market qualitynotably to store records of their trading systems for a minimum of five years and theimplementation of measures to prevent market distortionUnfortunately we can not infer the share of market participants engaging in HFT


andor algorithmic trading from our data as this number is even tough to measureat regulated stock exchanges5

Similar to Biais Hillion and Spatt (1995) we compute the conditional probabilitiesof LOB events Our results are presented in Table 54 The variation of conditionalprobabilities in each column of Table 54 indicates that order book events are notstatistically independent from previous order book events

54 Limit Order Book Characteristics in Cryptocurrency Mar-kets

541 The Shape of the Average Order Book

Incoming limit orders are stored in the LOB until they are either executed or can-celed The sum of the demanded or supplied quantity of the traded assets currentlyavailable in the LOB at a given price level defines the order book volume at thatprice level and represents the current queue size The sum over all price levels isnow referred to as the depth of the order book As incoming orders are determinedby market participants so is the volume of the order book at a given price levelIn Figure 52 the sum of the squares at each price level represents the volume of theorder book at that price levelOur analysis of LOBs provides insights into the microstructure of cryptocurrencymarkets and serves two major purposes First studying market behavior at cryp-tocurrency exchanges is crucial in order to understand whether cryptocurrenciescan extend the bandwidth of options for corporate finance by providing an alter-native way of raising capital Second identifying market behavior at cryptocur-rency exchanges analogous to those of security exchanges would indicate that majormarket characteristics are determined by intrinsic (economic) human behavior andare less determined by asset specific characteristics Thus studying cryptocurrencyLOBs is important for the fields of market microstructure but also yields valuableempirical insights for corporate and behavioral finance theory In this section westudy whether the behavior of market participants creates similar patterns in cryp-tocurrency limit order books as have been found in stock marketsExaminig stock markets Potters and Bouchaud (2003) argue that the shape of theaverage order book is not clear a priori While most of the incoming orders arrivein proximity to the current bid or ask price an order placed close to the currentprice has a larger probability to be executed and disappearing from the order bookBouchaud Meacutezard and Potters (2002) find the time-averaged shape of the limit or-der book to be characterized by a maximum distant to the current bid-ask spread

5Nonbinding estimations for the amount of HFT as a share of total trading activity mostly lie closeto the 50 range for stock exchanges Ultimately one may not abandon the fact that all algorithmictrading strategies act according to rules made by humans pursuing the goal to generate profit thusthey do not act independent and are just an extension of the human capabilities


TABLE 52 Sample Overview and Meta Statistics of Trading Data

Notes We recorded data from 18th of April 2018 until 31th of August 2019 of live order book updatesDuring this time frame we aggregate 60 GB of raw data tracking every event in the order book ofthe respective currency pair The deviation from Recorded (Days) and Full Data (Days) stems fromexogenous events eg forced operating system updates or a reboot of the server used for data storageWe exclude days where we are missing data due to those events Trading is allowed nonstop OneDay refers to a full 24h cycle rather than a traditional trading day which is dependent on the openinghours of the respective exchange BTC refers to Bitcoin BCH refers to Bitcoin Cash ETH refers toEthereum USD refers to US Dollar There are no records of currency pairs including BCH in April2018 as we were not able to receive live order book updates in this time frame We observe someinterrupts tracking the order flow since mid-2019 Upon closer inspection these interrupts do notfollow a pattern and appear to be unsystematic We tackle this data issue by controlling for monthlyfixed effects in our empirical analysis thus this occurrence does not raise any concerns regarding theunbiasedness of our results

Currency Pair Recorded Full Data Avg Filesize Avg No of Avg Length of of OF(Days) (Days) (MBDay) Sessions one Session Recorded

(per Day) (min) (per Day)

April 2018BTCUSD 12 11 262 24 19 31ETHUSD 12 11 239 27 18 33BCHUSD ndash ndash ndash ndash ndash ndash

May 2018BTCUSD 31 30 252 23 37 57ETHUSD 31 30 251 28 14 27BCHUSD 11 10 233 34 3 8

June 2018BTCUSD 22 20 233 24 26 40ETHUSD 24 23 244 25 19 31BCHUSD 23 22 237 23 45 41

July 2018BTCUSD ndash ndash ndash ndash ndash ndashETHUSD 6 5 105 20 7 9BCHUSD 13 13 80 25 2 3

February 2019BTCUSD 17 15 261 25 19 32ETHUSD 16 13 242 17 57 66BCHUSD 17 15 207 4 310 83


July 2019BTCUSD 11 11 70 13 6 5ETHUSD 11 11 69 12 12 10BCHUSD 11 11 68 12 20 17

August 2019BTCUSD 17 17 69 12 8 7ETHUSD 17 17 68 10 19 14BCHUSD 17 17 68 9 36 25


TABLE 53 Breakdown of Order Types and Cancellation Rates

Notes This table provides conditional probabilities for specific events in the order book If a neworder arrives in the order book it is labeled as received and be either a market or a limit orderEach order remains in the order book until it is done which can be either due to a cancellationor because the order was matched (filled) The interpretation is shown on the following exam-ple The value 9958 in the first row and column (BTCUSD Panel A Sell Side Received Limit)is the probability of an incoming sell order in the BTCUSD order book to be a limit order ieP(Order Type = limit|Currency Pair = BTCUSD Type = received Side = sell) = 9958 DataSource Cryptocurrency limit order books obtained from April to August 2019 (see Table 52 for a dataoverview)

BTCUSD ETHUSD BCHUSD Mean

Panel A Sell Side

Received

Limit 9958 9976 9991 9975

Market 042 024 009 025

Done

Canceled 9836 9844 9881 9854

Filled 164 156 119 146

Panel B Buy Side

Received

Limit 9967 9979 9968 9971

Market 033 021 032 029

Done

Canceled 9922 9936 9877 9912

Filled 078 064 123 088


TAB

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54

Con

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Prob

abili

ties

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Type

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Type

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This finding is surprising as the probability of an incoming order to be placed is high-est at the best price The authors find this shape across different stocks listed at theParis Bourse In addition they find that the shape of the time-average order book isroughly symmetric between the bid and the ask side6 Potters and Bouchaud (2003)analyze the average shape of the order book of two exchange traded funds that trackthe NASDAQ and the SampP500 performance respectively confirming a maximum ofthe queue size lying away from the bid-ask spread for one of the ETFs For the otherETF however the queue has a maximum at the current bid-ask spread7

In order to buy a cryptocurrency (eg Bitcoin Bitcoin Cash or Ethereum) a buyerneeds to find someone to trade the respective currency for another currency eg USDollar Bringing together buyers and sellers of cryptocurrencies is the purpose ofcryptocurrency exchanges which operate a LOB following the same set of rules asstock exchanges in particular the price-time priority and the first-come first-servedprinciple In this section we restrict our work and empirical analysis to the LOBof three major cryptocurrencies Bitcoin (BTC) Bitcoin Cash (BCH) and Ethereum(ETH) all of which can be bought and sold for US Dollar at a cryptocurrency ex-change We exclusively consider US Dollar order books to retain a common denom-inator allowing to compare our empirical results across cryptocurrencies

542 The Bitcoin Order Book

Below we analyze characteristics of the BitcoinUS Dollar limit order book Ex-isting literature mainly focuses on the empirical shape of the order book of stocksWhile Biais Hillion and Spatt (1995) and Naeligs and Skjeltorp (2006) examine theslope of the aggregated order book and potential connections to volume and volatil-ity Bouchaud Meacutezard and Potters (2002) show that a snapshot of the order bookcan deviate substantially from its average shapeThe slope of the order book is derived by computing the gradient for the additionalsupplied or demanded volume for deeper levels in the order book Economicallythe slope of the order book is the elasticity partqpartp describing how quantity (q) pro-vided in the order book changes as a function of the price (p) (Naeligs and Skjeltorp2006 p 415)First we plot the empirical order book volume and the aggregated volume of the

BTCUSD LOB as a function of the absolute distance measured in ticks (∆) towardsthe best price8 The results are shown on a daily-average basis in Figure 53 andFigure 54 for the ask and bid side of the LOB respectively It can be seen that theavailable volume is highest directly at the bid-ask spread Both sides of the order

6These findings have been empirically confirmed by Zovko and Farmer (2002) and Mike andFarmer (2008)

7The authors argue that the observed deviation may be due to the order book of this ETF not beingthe dominant player at NASDAQ as Island ECN is just one of many trading platforms of NASDAQcovering only 20 of the total trading volume of this specific ETF (Potters and Bouchaud 2003 p136)

8One tick corresponds to one US Dollar cent in the BTCUSD LOB


Day 1Day 11

Day 21Day 31Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Obersvation DayAsk Volu

me (in BTC)

ΔDay 1

Day 11

Day 21

Day 31

Day 41

Day 51

05

10152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observa

tion Day

Aggrega

ted Ask

Volume

(in BTC

)

Δ

Notes The figure shows both the average volume (left) and the average aggregated volume (right)dependent on the distance ∆ to the best ask price for the first 350 ticks on a daily basis across thesample period We derive the data by computing the average volume at each tick per day based onsnapshots of the order book taken every 10 minutes Accordingly each single data point in the abovechart represents the average value of 144 observations Most of the volume is available directly at thespread however a pattern of slightly higher volume manifests surrounding definite numbers (100 200and 300 ticks away from the best ask price) emerge

FIGURE 53 Ask Volume and Aggregated Ask Volume of the BTCUSD Limit Or-der Book relative to the Distance (∆) towards the best Ask Price

book seem to behave almost symmetrical however the aggregated volume at thebid side increases more steadily in ∆ Looking at the shape of the aggregated or-der book in Figure 53 and Figure 54 we observe substantial variation of the shapeacross observation days raising the question if there is information hidden in theslope of the order bookFor both the bid and ask side a higher average volume at multiples of 100 ticksaway from the best price can be observed This effect can be found consistentlyacross the observation period and is shown in more detail in Figure 55 A highaverage volume across the whole sample period can be observed at round figuresespecially for ∆s that are a multiple of 100 Preferences for round figures have beendocumented in the financial literature before notably Corwin (2003) finds that theunderpricing in seasoned equity offers is significantly related to underwriter pric-ing conventions such as price rounding and pricing relative to the bid quote Fur-ther links between numeric fluency and human preferences have been documentedby Kettle and Haumlubl (2010) and we are confident that the observed pattern in LOBvolumes can be linked to human preferences as well9

Next we examine the slope of the BTCUSD LOB The slope of the order bookrepresents the elasticity of the market supply and demand of the respective cryp-tocurrency We test how the aggregated order book volume increases in ∆ by com-paring the empirical fit of three alternative sets of models The first model assumes alinear relationship between aggregated order book volume and ∆ the second model

9We test the statistical significance of this finding in Section 545


Day 1Day 11

Day 21Day 31

Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation D

ay

Bid Volume (

in BTC)

ΔDay 1

Day 11Day 21

Day 31Day 41

Day 51

0510152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observatio

n Day

Aggregated

Bid Volum

e (in BTC)

Δ

Notes The figure shows both the average volume (left) and the average aggregated volume (right)dependent on the distance ∆ to the best bid price for the first 350 ticks on a daily basis across thesample period We derive the data by computing the average volume at each tick per day based onsnapshots of the order book taken every 10 minutes Accordingly each single data point in the abovechart represents the average value of 144 observations Most of the volume is available directly at thespread however a pattern of slightly higher volume surrounding definite numbers (100 200 and 300ticks away from the best ask price) emerge

FIGURE 54 Bid Volume and Aggregated Bid Volume of the BTCUSD Limit OrderBook relative to the Distance (∆) towards the best Bid Price

assumes a logarithmic relation while the third model assumes a square root rela-tion10

1 Aggregated Average LOB VolumeBTCUSD(∆) = β0 + β1 times ∆

2 Aggregated Average LOB VolumeBTCUSD(∆) = β0 + β1 times ln(∆)

3 Aggregated Average LOB VolumeBTCUSD(∆) = β0 + β1 timesradic

∆

The empirical fit is presented in Table 55 We find that the logarithmic model per-forms worst for both the bid and ask side of the BTCUSD LOB Surprisingly ndash withan R2 of 9885 and 9888 ndash the square root specification beats the linear modelfor both the ask and bid side of the order book by 623 and 114 percentage pointsrespectively Considering only the most relevant part of the aggregated order book(∆ le 100) we find the square root model to still fit better than the linear modelhowever the difference in explanatory power between the two models diminishesConsidering only the first 100 ticks away from the current best price the squareroot specification yields an R2 which is 145 (ask side) and 086 (bid side) percentagepoints higher than in the linear modelOur results suggest that the slope of the BTCUSD LOB decreases when the distancetowards the best price increases This finding is valid for both sides of the orderbook Close to the best price however a steady slope appears to be a reasonable ap-proximation for the slope of the order book Our results also indicate that assuming

10Based on our graphical analysis (see Figure 53 and Figure 54) we do not test for other functionalforms However we test whether the functional form is concave or convex in Section 59 Note thatthe aggregated average LOB volume increases in ∆ by definition


a logarithmic relationship to describe the link between the aggregated average orderbook volume and ∆ is not feasible

Notes The left (right) figure shows the aggregated bid (ask) volume relative to the best bid (ask)price across the observation period for the BTCUSD limit order book for the first 2000 ticks Volumedirectly at the spread is omitted due to scaling Volume peaks occur at round numbers at both sides ofthe BTCUSD limit order book

FIGURE 55 Average Ask and Bid Volume of the BTCUSD Limit Order Book rel-ative to the Distance Delta (∆) towards the Best Bid or Ask Price


TABLE 55 Slope of the Aggregated Order Book for BTCUSD

Notes The table shows the slope of the average order BitcoinUS Dollar LOB across our sample pe-riod The slope is derived from a linear regression for different model specifications Model specifi-cations (11)-(32) consider the slope of the supply side Respectively Model specifications (13)-(34)show the empirical results for the demand side Each observation has a high level of confidence asit denotes the average aggregated volume of the order book ∆ ticks away from the best price Wemeasure the volume every ten minutes continuously across our sample period

Ask Side

Sample ALL ∆ le 100

Model (11) (21) (31) (12) (22) (32)

∆ 002lowastlowastlowast 002lowastlowastlowast

Std Err 0 0

ln(∆) 26022lowastlowastlowast 062lowastlowastlowast

Std Err 041 002radic


Std Err 0 0

Constant yes yes yes yes yes yes

N 50413 50412 50413 101 100 101

R2 9262 8892 9885 9598 8653 9743

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9774 8263 9888 9700 8667 9786

p lt 010 p lt 005 p lt 001


543 The Bitcoin Cash Order Book

Figure 56 and Figure 57 show the average ask and bid volume and the aggregatedaverage ask and bid volume per observation day of the BCHUSD limit order bookWe find that the shape of the order book is rather symmetrical between the bid andask side Compared to the BTCUSD aggregated order book however more volumeseems to be located away from the bid-ask spread resulting in a steeper slope of theorder book Similar to the previous section we compute three different models toexplain the slope of the order book as a function of ∆ We use the method of ordinaryleast squares (OLS) to compute the fit of three different models imposing a linearlogarithmic or square-root relation between the increase in ∆ and the aggregatedaverage order book volume

1 Aggregated Average LOB VolumeBCHUSD(∆) = β0 + β1 times ∆

2 Aggregated Average LOB VolumeBCHUSD(∆) = β0 + β1 times ln(∆)

3 Aggregated Average LOB VolumeBCHUSD(∆) = β0 + β1 timesradic

∆

Our results are presented in Table 56 In line with our previous findings for theBTCUSD order book we find the logarithmic model to possess the worst explana-tory power Comparing the linear to the square-root model we find that the impliedrelation depends on the number of ticks taken into account While both models donot differ much in their explanatory power the square-root relation appears to betterexplain the increase in order book volume for both the bid and ask side if all levelsof ∆ are taken into account Considering only the first 100 ticks closest to the bid-askspread the linear model outperforms the square-root model by three (ask side) andfive (bid side) percentage points respectivelyMotivated by the volume pattern which emerged in the BTCUSD order book wecompute the average order book volume across observation days dependent on ∆(Figure 58) Again we find large peaks at round figures for ∆ especially at mul-tiples of 100 at both sides of the BCHUSD order book Surprisingly even largerpeaks emerge at 500 1000 and 1500 ticks away from the best price It is importantto note that these peaks represent average values and are unlikely to be outliers asthey can be observed consistently across our observation period We further findthat the average shape of the order book appears to have a maximum away fromthe bid-ask spread which also has been observed in stock markets by BouchaudMeacutezard and Potters (2002) In the BCHUSD LOB the average volume generallyincreases in ∆ between 0 and approximately 250 ticks before it slowly declines from250 ticks onward Bouchaud Meacutezard and Potters (2002) propose an analytical ap-proximation to compute the average order book concluding that [T]he shape of theaverage order book therefore reflects the competition between a power-law flow oflimit orders with a finite lifetime and the price dynamics that removes the orders


Day 1Day 4Day 7Day 10Day 13Day 16Day 19Day 220

4

8

12

16

20

24

28

32

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation DayAsk Volume (

in BCH)

Δ Day 1Day 5Day 9Day 13Day 17Day 210

50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation Day

Aggregated Ask Vo

lume (in BCH)

ΔNotes The figure shows both the average volume (left) and the average aggregated volume (right)dependent on the distance ∆ to the best ask price for the first 350 ticks on a daily basis across thesample period We derive the data by computing the average volume at each tick per day based onsnapshots of the order book taken every 10 minutes Accordingly each single data point in the abovechart represents the average value of 144 observations Most of the volume is available directly at thespread however a pattern of slightly higher volume manifests surrounding definite numbers (100 200and 300 ticks away from the best ask price) emerge

FIGURE 56 Ask Volume and Aggregated Ask Volume of the BCHUSD LimitOrder Book relative to the Distance (∆) towards the best Ask Price

Day 1Day 4Day 7Day 10Day 13Day 16Day 190

2

4

6

8

10

12

14

16

18

20

22

24

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation DayBid Volum

e (in BCH)

ΔDay 1Day 3Day 5Day 7Day 9Day 11Day 13Day 15Day 17Day 19Day 21

0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observatio

n Day

Aggregated

Bid Volum

e (in BCH)

ΔNotes The figure shows both the average volume (left) and the average aggregated volume (right)dependent on the distance ∆ to the best bid price for the first 350 ticks on a daily basis across thesample period We derive the data by computing the average volume at each tick per day based onsnapshots of the order book taken every 10 minutes Accordingly each single data point in the abovechart represents the average value of 144 observations Most of the volume is available directly at thespread however a pattern of slightly higher volume manifests surrounding definite numbers (100 200and 300 ticks away from the best ask price) emerge

FIGURE 57 Bid Volume and Aggregated Bid Volume of the BCHUSD Limit OrderBook relative to the Distance (∆) towards the best Bid Price


close to the current price These effects lead to a universal shape which will presum-ably hold for many different markets[] (Bouchaud Meacutezard and Potters 2002p11) The authors further indicate that preliminary results show that the same be-havior can be observed in futures marketsWe provide empirical evidence that a hump away from the current mid point canalso be observed in cryptocurrency markets However Bouchaud Meacutezard and Pot-ters (2002) do not report volume peaks at round figures making a significance testfor our findings inevitable To analyze this phenomenon we perform statistical testsacross the cryptocurrency order books for Bitcoin Bitcoin Cash and Ethereum inSection 545

Notes The left (right) figure shows the aggregated bid (ask) volume relative to the best bid (ask) priceacross the observation period for the BCHUSD limit order book for the first 2000 ticks Volumedirectly at the spread is omitted due to scaling Volume peaks occur at round numbers at both sides ofthe BCHUSD limit order book

FIGURE 58 Average Ask and Bid Volume of the BCHUSD Limit Order Bookrelative to the Distance Delta (∆) towards the Best Bid or Ask Price


TABLE 56 Slope of the Aggregated Order Book for BCHUSD

Notes The table shows the slope of the average order Bitcoin CashUS Dollar LOB across our sampleperiod The slope is derived from a linear regression for different model specifications Model speci-fications (11)-(32) consider the slope of the supply side Respectively Model specifications (13)-(34)show the empirical results for the demand side Each observation has a high level of confidence asit denotes the average aggregated volume of the order book ∆ ticks away from the best price Wemeasure the volume every ten minutes continuously across our sample period

Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0

ln(∆) 77522 789lowastlowastlowast



Std Err 0 0


N 15178 15177 15178 101 100 101

R2 9574 8871 9992 9933 8198 9633

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 002 005


N 15178 15177 15178 101 100 101

R2 9822 8450 9947 9943 7864 9436

p lt 010 p lt 005 p lt 001


544 The Ethereum Order Book

The internal cryptocurrency of Ethereum (ETH) can be traded on cryptocurrencyexchanges against fiat money or other cryptocurrencies In this section we focusspecifically on the ETHUSD limit order book to obtain comparability with the Bit-coin and Bitcoin Cash order book ie one tick (∆) resembles a buy or sell price oneUS Dollar cent lower or higher than the current best bid or ask price in the ETHUSDLOBFigure 59 and Figure 510 show the volume and the aggregated ask volume depen-dent on the distance towards the best price for the bid and ask side respectively Wefind both sides of the order book to behave almost symmetrical Interestingly vol-

Day 1Day 6

Day 11Day 16Day 21Day 26Day 31Day 36Day 41Day 46Day 51

020406080

100120140160180

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observati

on Day

Ask Volum

e (in ETH

)

Δ Day 1Day 4Day 7Day 10Day 13Day 16Day 19Day 22Day 25Day 28Day 31Day 34Day 37Day 40Day 43Day 46Day 49Day 52

05001000150020002500

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation DayAggrega

ted Ask Volume

(in ETH)


FIGURE 59 Ask Volume and Aggregated Ask Volume of the ETHUSD Limit Or-der Book relative to the Distance (∆) towards the best Ask Price

ume peaks at round figures seem to be omnipresent in the ETHUSD order booksupporting our hypothesis that this observation follows a pattern and is also observ-able across different assets To get a better picture of the phenomenon we computethe average volume at each ∆ across the observation period (Figure 511) For bothsides of the order book we find the available volume to be of magnitudes higherwhen ∆ is a multiple of 100 than at ∆s surrounding those numbers We also find thatndash analogous to the Bitcoin Cash order bookndash the average shape of the ETHUSDorder book appears to have a maximum away from the best bid or ask price oncemore supporting the applicability of the model proposed by Bouchaud Meacutezard andPotters (2002) in a different marketWe further find that the volume peaks seem to mimic the average shape of the orderbook ie only considering the volume at multiples of 100 we also find a maximumaway from the best ask or bid price This is especially observable for the ask side of


Day 1Day 6Day 11Day 16Day 21Day 26Day 31Day 36Day 41Day 46Day 51

020406080100120140

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation DayBid Volu

me (in ETH)


05001000150020002500

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation DayAggregate

d Bid Volume (in ETH

)Δ

Notes The figure shows both the average volume (left) and the average aggregated volume (right)dependent on the distance ∆ to the best bid price for the first 350 ticks on a daily basis across thesample period We derive the data by computing the average volume at each tick per day based onsnapshots of the order book taken every 10 minutes Accordingly each single data point in the abovechart represents the average value of 144 observations Most of the volume is available directly at thespread however a pattern of slightly higher volume manifests surrounding definite numbers (100 200and 300 ticks away from the best ask price) emerge

FIGURE 510 Bid Volume and Aggregated Bid Volume of the ETHUSD LimitOrder Book relative to the Distance (∆) towards the best Bid Price

Notes The left (right) figure shows the aggregated bid (ask) volume relative to the best bid (ask)price across the observation period for the ETHUSD limit order book for the first 2000 ticks Volumedirectly at the spread is omitted due to scaling Volume peaks occur at round numbers at both sides ofthe ETHUSD limit order book

FIGURE 511 Average Ask and Bid Volume of the ETHUSD Limit Order Bookrelative to the Distance Delta (∆) towards the Best Bid or Ask Price


the ETHUSD order book (Figure 511) However we find it difficult to explain thisobservation A behavioral explanation could be that there exists a group of lazyinvestors that does not care about incremental tick sizes and only considers a lessgranular price grid but still places orders based on a trade off between executionprobability and time priority We discuss and test this hypothesis in Section 545Finally we take a look at the slope of the ETHUSD order book Analogous to theanalysis of the BTCUSD and the BCHUSD LOB we compare the fit of three mod-els supposing three different functional forms of the relation between the averageaggregate order book volume and ∆

1 Aggregated Average LOB VolumeETHUSD(∆) = β0 + β1 times ∆

2 Aggregated Average LOB VolumeETHUSD(∆) = β0 + β1 times ln(∆)

3 Aggregated Average LOB VolumeETHUSD(∆) = β0 + β1 timesradic

∆

Similar to the Bitcoin and Bitcoin Cash LOB the logarithmic model yields the worstfit while the linear model has the highest explanatory power across all samples Themediocre performance of both the logarithmic and square-root model hints that theslope of the ETHUSD LOB might be better explained by a convex function11

545 The Lazy Investor Hypothesis

Based on the discovery of unusual high order book volume at specific ∆s away fromthe best price which we find consistently across all observed cryptocurrency LOBsduring the graphical analysis we formulate the following null hypothesis

The Lazy Investor Hypothesis There exists a group of cryptocurrency investorswhich disregard the full granularity of the price grid leading to a higher averagelimit order book volume at ∆ ticks away from the best price if ∆ is a multiple of 100

To our knowledge this anomaly has not yet been examined in the literature beforeWe test the statistical significance by constructing a binary variable I∆ which equalsone if the distance towards the best price is a multiple of 100 and zero otherwiseWe compute the coefficient of I∆ using OLS based on Equation 51

Limit Order Book Volume∆ = β0 + β1∆ + β2 I∆ + ε where (51)

I∆ =

1 i f ∆ = 100 mod(0)0 else

To take a linear decline of the average order book volume into account we include∆ as a control variable in our regression model If there exist lazy investors our

11We allow for a convex or concave shape and directly compare the Ethereum Bitcoin and BitcoinCash LOB in Section 546


TABLE 57 Slope of the Aggregated Order Book for ETHUSD

Notes The table shows the slope of the average order EthereumUS Dollar LOB across our sampleperiod The slope is derived from a linear regression for different model specifications Model speci-fications (11)-(32) consider the slope of the supply side Respectively Model specifications (13)-(34)show the empirical results for the demand side Each observation has a high level of confidence asit denotes the average aggregated volume of the order book ∆ ticks away from the best price Wemeasure the volume every ten minutes continuously across our sample period

Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 018 071


N 35395 35394 35395 101 100 101

R2 9861 8122 9844 9928 7496 9214

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 002




Std Err 046 0


N 35395 35394 35395 101 100 101

R2 9981 7718 9712 9849 7191 8995

p lt 010 p lt 005 p lt 001


estimated coefficient for β2 should deviate significantly from zero The results foreach cryptocurrency are presented in Table 58 We find that the coefficient for ourconstructed variable is indeed highly significant for both sides of the BTCUSDthe BCHUSD and the ETHUSD LOB indicating that the LOB volume at specificlevels relative to the best price appears to be dependent on whether the level is amultitude of 100 ie a round figure

Key Finding 1 Based on our analysis of the limit order books of BTC ETH andBCH our findings support the lazy investor hypothesis The empirical results arehard to bring in line with the concept of rational agents yet the pattern could beexplained by a human preference for round figures dominating the desire to achievethe best possible price

The observed volume peaks also imply a market stabilizing purpose related to pricemovements If the price moves into the direction of a volume peak the peak acts asa dam that curtails heavy price movements eg the price can not drop lower than100 ticks of the current price without the volume at 100 ticks away from the currentprice being matched against incoming orders first12 Based on our findings futureattempts to explain the shape of the average order book should try to take this effectinto account

TABLE 58 Volume peaks in Cryptocurrency Limit Order Books

Notes This table shows the regression results for the average volume in the Bitcoin Bitcoin Cash andEthereum limit order book I∆ represents a dummy variable which equals one if the distance towardsthe best price (∆) can be divided by 100 without remainder

BTCUSD BCHUSD ETHUSD

Ask Side Bid Side Ask Side Bid Side Ask Side Bid Side

I∆ 028 035 158 151 811 901

Std Err 0 0 004 003 008 136

∆ yes yes yes yes yes yes


N 50412 50412 15177 15177 35394 35394

R2 3105 600 1205 1606 2358 279

p lt 010 p lt 005 p lt 001

12Note that this is a hypothetical case It is likely that some market participants will cancel their limitorder before the price reaches the volume peak Our finding would also induce that the probability ofprice movements of increasing magnitude does not decrease steadily


546 Convexity of the Order Book Slope

Motivated by our finding that the functional form of the demand and supply curvesslightly vary across cryptocurrencies (see Table 55 56 and 57) we test whethercryptocurrency order books have a concave or convex demand and supply curve13

Table 55 56 and 57 equally show that we can rule out a logarithmic relation Thuswe test for convexity by allowing the exponent to vary in a polynomial model whichcan be regarded as an extension to the proposed square-root relation model

Aggregated LOB Volume∆ = LOB Volume at the spread + ∆β (52)

By applying the natural logartihm to Formula 52 we can estimate β using the

Notes The upper left picture shows the theoretical aggregated order book volume implied by Equa-tion 52 dependent on the β-parameter The following pictures show the actual aggregated order bookvolume of the BTCUSD order book derived from our data Each data point at the respective tick (∆Delta) is calculated as the average order book volume at that tick derived from 10 minute snapshotsacross 94 observation days ie each data point is an average of roughly 13500 observations Con-sidering all tick levels (upper right picture) the total aggregated order book volume of the BTCUSDcurrency pair appears to be concave implying a β-parameter of less than one

FIGURE 512 ConvexityConcavity of the Aggregated BTCUSD Limit OrderBook

13The aggregated average order book volume of the ask (bid) side gives the available total volumeavailable at this price This means that the aggregated average volume can be interpreted as the de-mand (supply) curve of the respective cryptocurrency


method of OLS Consequently β lt 1 (β gt 1) indicates a concave (convex) demandor supply curve The results are presented in Table 59 and show that Bitcoin andBitcoin Cash demand and supply curves are concave while the Ethereum supplyand demand curve can be characterized as slightly convex The direction of our re-sults do not change when we restrict our sample to the first 100 ticks away from thebest price However it is worthwhile to note that the measured β-coefficients areclose to one justifying a linear approximation in empirical analysis without losingmuch of the explanatory power

Key Finding 2 We conclude that it is reasonable to approximate the slope of theorder book linearly

55 Informativeness of Order Book Characteristics

In the previous sections we mainly examine static limit order book characteristicsIn this section we are focusing on dynamic relations of the LOB and investigatea potential link between the slope of the order book and cryptocurrency returnsWe also hypothesize non-mechanic connections between the order book slope andtrading activityNaeligs and Skjeltorp (2006) find a negative link between the order book slope andvolume volatility and the correlation between volume and volatility in Norwegianstock markets and show that the slope can be regarded as a proxy for disagreementamong investors We follow the approach of Naeligs and Skjeltorp (2006) in that wealso investigate three groups of models relating to the order book slope (SLOPEit)

1 Price changeit = f (SLOPEit )

2 Number of tradesit = f (SLOPEit )

3 Correlation(Price changeit Number of tradesit) = f (SLOPEit )

The first set of models examines the relation between the price change of a cryptocur-rency and the slope of the order book In an efficient market price changes occurwhen new information about the value of the underlying assets emerge Observinga link between price changes and the slope of the order book indirectly suggests thatthe volume of the order book ie the aggregated supply and demand possibly con-tains information about the value of the underlying assetThe second set of models considers the relation between the number of trades andthe slope of the order book Each trader has to choose between buying directly orplacing a limit order We are interested in whether traders consider the existing vol-ume in the order book when placing a new order A link between the slope of theorder book and trading activity could shed light on the dilemma a trader faces whendeciding at what price level he should place a new order


TABLE 59 ConcavityConvexity of Aggregated Cryptocurrency Limit OrderBooks

Notes We test the concavity (convexity) of the average aggregated volume of of the Bit-coin Bitcoin Cash and Ethereum LOB across our sample period Our initial equation equalsAggregated LOB Volume∆ = LOB Volume at the spread + ∆β We estimate β using OLS and trans-forming the initial equation ln(Aggregated LOB Volume∆ minus Volume at the spread) = β lowast ln(∆) Conse-quently β lt 1 (β gt 1) indicates a concave (convex) demand or supply curve

Ask Side


Currency (USD) BTC BCH ETH BTC BCH ETH

β-Coeff 064 086 104 017 074 118

Std Err 0 0 0 001 0 001

Constant no no no no no no

N 50412 15177 14 35394 100 100 100

R2 9985 9994 9994 8853 9982 9950

p-Value (H0 β gt 1) lt001 lt001 gt099 lt001 lt001 gt099

Bid Side



β-Coeff 065 087 108 013 059 109

Std Err 0 0 0 001 001 001


N 50412 15177 35394 100 100 100

R2 9971 9996 9999 7166 9926 9950


p lt 010 p lt 005 p lt 001


The third set of models examines the link between the correlation between pricechange and the number of trades (volume-volatility relation) and the slope of theorder book The volume-volatility relationship is well documented for different fi-nancial markets including stocks currencies oil futures and other derivatives (Fos-ter 1995 Fung and Patterson 1999 Sarwar 2003 and Naeligs and Skjeltorp 2006) Therelation can be explained assuming that new information is not incorporated inprices directly but over time However the relation could also be explained in thecase that traders do not agree on the impact of new information14 Studying the im-pact of the order book slope on this relation could yield insight into what causes thevolume-volatility relation

551 Measuring the Slope of the Limit Order Book

We define the average aggregated LOB volume up to a price level of ∆ ticks awayfrom the best price for the ask side of the order book as

Avg aggr LOB Volume∆asktj =∆

sumi=1

(1N

N

sumn=1

VOLinasktj

)(53)

VOLinasktj gives the ask side LOB volume of currency j at a price level Ask price + iduring the n-th order book snapshot at day t As we capture a snapshot of the LOBevery ten minutes N amounts to 144 snapshots per dayIn order to create our slope variable we estimate the following regression modelusing OLS

ln(Avg aggr LOB Volume∆asktj) = β0 + βasktj times ln(Ask Price∆) (54)

βasktj is the estimated elasticity partqpartp of the aggregated LOB volume with respect to

price and resembles our slope measure SLOPEasktj A higher value of βasktj resem-bles a steeper order book We compute the β-parameter for both sides of the LOBfor each currency and each day The resulting slope measure can be represented inmatrix notation

SLOPEask =

βaskt=1BTC βaskt=1BCH βaskt=1ETH


(55)

We repeat the above steps analogous for the bid side to compute the average slope ofthe LOB We receive our final slope measure by computing the average daily slopeof the LOB

14This argumentation closely resembles the second interpretation of the widely used illiquidity mea-sure proposed by Amihud (2002)


SLOPE =SLOPEask + SLOPEbid

2(56)

We compute SLOPE considering two different sets of data SLOPE100 is calculatedusing only the first 100 ticks away from the best bid or ask price thus representingthe slope of the inner order book whereas SLOPE10000 includes price levels of upto 10000 ticks ndash ie up to 100 USD ndash away from the best bid or ask price capturinginformation potentially hidden in the depths of the order book (the deeper slope)

552 Order Book Slope and Volatility

In efficient markets price jumps occur through the arrival of new information thatimpacts the value of an asset If all investors agree on the impact of the new informa-tion price adjustments happen without trading Investors cancel existing limit or-ders and place new limit orders around the new equilibrium price level consideringthe updated information state However if there is no consensus among investorsabout the price impact of the new information trading takes place until all investorsthat do not agree on the price impact sold or bought their assets thereby moving theprice to itrsquos new equilibrium where consensus is reached againWe capture daily price jumps by computing the daily volatility of each cryptocur-rency according to the following formula

Volatilityit =

∣∣∣∣ LOB Mid pricet

LOB Mid pricetminus1minus 1∣∣∣∣ (57)

In general volatility is expected to be higher in less liquid assets as the supply anddemand side of the LOB is not thick enough to fulfill large market orders withoutexecuting limit orders at deeper price levels moving the mid price in consequenceSpontaneous demand or supply of large quantities lead to larger price changes inilliquid assets ie we would expect the SLOPE of illiquid assets to be more gentlea priori imposing a positive relationship between SLOPE and liquidity When in-vestigating the link between the order book slope and volatility we also control forilliquidity as illiquidity and volatility are likely correlatedWe include trading activity in our regression by deriving the number of trades perday based on Equation 58 We include a scaling factor in Equation 58 to make thenumber of trades comparable across days and cryptocurrencies (see Table 52)

N o f Tradesit = Recorded Tradesit times1440

Recorded Minutesit(58)

We also compute the average trade size per day for each cryptocurrency based onEquation 59

Trade Sizeit =Trading Volumeit

N o f Tradesit(59)


We further compute the average bid-ask spread (Spreadit) from our data and in-clude the market capitalization in itrsquos logarithmic form (ln(MCAP)it) The variablesln(MCAP)it and Spreadit are closely tied and proxy for liquidity We estimate thefollowing linear model

Volatilityit = β0 + β1SLOPE100it + β2SLOPE10000it + β3N o f Tradesit

+ β4Trade Sizeit + β5ln(MCAP)it + β6Spreadit + ci + uit (510)

Using fixed-effects regressions we get rid of time-constant unobserved effects ciTable 510 shows the results for six different model specifications We find that theslope of the inner order book has a significant positive effect on volatility in Model 1however this effect diminishes when controlling for the number and size of trades

Key Finding 3 We conclude that the slope of the inner LOB can not explain re-turn variation

Models 4-6 consider the LOB volume of the first 10000 ticks and reveal a positivelink between the deeper order book slope and return variation The effect remainssignificant at the 10 level across all three model specifications This finding indi-cates that a steeper order book slope can be associated with higher volatility Thisfinding seems to be contradictory at first as a steeper slope should prevent largeprice jumps as the LOB holds enough volume to serve large market orders withoutshifting prices too much However as we control for the trading activity and liq-uidity the volatility increase due to a steeper slope is likely caused by informationwhere investors agree upon the price impact and consequently adjust their activelimit orders

Key Finding 4 Our findings indicate that a steeper slope can be associated withmore non-trading volume ie investors adjust their limit orders based on new un-ambiguous information more actively when there is more LOB volume close to thespread

This finding could be explained by a lower execution probability of a limit orderceteris paribus when the slope is steep A steep slope indicates that a lot of the LOBvolume is centered around the spread reducing the execution probability of limitorders deeper in the LOB Traders observe this reduced execution probability andadjust their limit orders accordingly Through this channel limit orders deeper inthe LOB are still relevant for the price formation process even though they do notaffect the price mechanically


We further find that more trades and larger average trade size lead to higher volatil-ity The effect remains significant when controlling for ln(MCAP) and Spread Ar-guably trading activity is often interpreted as a sign of liquidity and we would ex-pect a decrease in volatility when trading activity is high However we controlfor illiquidity by including ln(MCAP) and Spread Our results hint a very nervouscryptocurrency market environment where many hectic trades are executed in ashort period of time leading to huge price jumps

TABLE 510 Volatility and the Slope of the Order Book

Notes This table shows the fixed effects regression results for Equation 510 Volatility is measured asthe daily absolute return of a cryptocurrency Coefficients of N of trades SLOPE100 and SLOPE10000are multiplied by 105 for better readability The coefficient of Trade size ln(MCAP) and spread is multi-plied by 103

Model (1) (2) (3) (4) (5) (6)

SLOPE100 047 036 040

(265) (157) (148)

SLOPE10000 952 943 950

(172) (171) (166)

N of trades 004 004 005 005

(386) (361) (520) (410)

Trade size 073 071 071 073

(404) (382) (405) (390)

ln(MCAP) -287 273

(minus031) (035)

Spread -163 -319

(minus010) (minus02)

Month fixed effects yes yes yes yes yes yes

N times T (currency-days) 231 186 186 231 186 186

R2 1120 2920 2920 960 2930 2940

p lt 010 p lt 005 p lt 001 t-statistics in parentheses

553 Order Book Slope and Trading Activity

In this section we examine the link between the order book slope and trading activ-ity measured by the number of trades per day We compute four different models


based on Equation 511 Our results are presented in Table 511

N o f Tradesit = β0 + β1SLOPE100it + β2SLOPE10000it + β3Trade Sizeit

+ β4ln(MCAP)it + β5Spreadit + ci + uit (511)

Key Finding 5 We find that the order book slope is significantly positive relatedto trading activity Interestingly this relation changes sign and becomes negativewhen considering the slope measure computed from the first 10000 ticks instead ofrestricting the data used for computing the slope to the first 100 ticks

With regard to causality we argue that it is reasonable to assume that an investorinspects the order book before placing an order and not afterwards assuming hisgoal is to make an educated trading decision If this is the case it is plausible thatthe slope of the order book affects trading activity and not vice versa Naeligs andSkjeltorp (2006) find the same astounding result in Norwegian stock markets andconclude that the order book slope contains different information based on the depthof the order book used to compute the slope Our result confirms their findings andshows that a sign change can be observed in cryptocurrency markets as well evenby using an alternative approach to calculate the slopeWe further find that the average trade size does not seem to influence trading ac-tivity and that the number of trades is higher when the spread increases A narrowbid-ask spread generally reduces trading costs which should amplify trading per seHowever upon closer inspection we find that the spread in our data equals the mini-mum tick size of 001 USD in 4202 of all observations whereas the average spreadis 050 USD indicating that when the spread deviates from the minimum tick sizethe deviation is quite large Such a change in the spread could be interpreted bymarket participants as a trading signal which would explain our empirical results

554 Order Book Slope and the Volume-Volatility Relation

Next we examine the interplay between the volume-volatility relation and the slopeof the order book The volume-volatility relation is a well known phenomenonobserved across many markets describing the empirical observation of high pricevolatility coupled with high trading volume which has been confirmed by a varietyof studies (see Karpoff 1987)In our data we find a positive correlation between volatility and daily trading vol-ume of 1919 and a positive correlation between volatility and the number of tradesof 2644 indicating that the volume-volatility relation can be observed in cryp-tocurrency markets as well We are interested in the cause of this relationship anddifferent theoretical models have been proposed mainly focusing on market effi-ciency the informedness of traders and speculative trading (Glosten and Harris


TABLE 511 Trading Activity and the Slope of the Order Book

Notes This table shows the fixed effects regression results for Equation 511 The number of trades (Nof trades) is the dependent variable

Model (1) (2) (3) (4)

SLOPE100 1203 416

(873) (223)

SLOPE10000 6792 -6736

(143) (minus171)

Trade size -4395 -12390


ln(MCAP) 2416250 3267030

(403) (677)

Spread 453150 553640

(441) (545)

Month fixed effects yes yes yes yes

N times T (currency-days) 186 186 186 186

R2 5520 6340 3650 6290



1988 Foster and Viswanathan 1995) We test whether the order book slope ie theavailability of potential trading volume can help to explain this relation Naeligs andSkjeltorp (2006) find that the slope of the order book is significantly negatively re-lated to the volume-volatility relation concluding that a stronger volume-volatilityrelation is associated with a flat slope of the order book However the authors do notdirectly interpret their results Similar to Naeligs and Skjeltorp (2006) we investigatethis relationship by computing the daily correlation coefficient Corr(N o f tradesit|Rit|) measured over a month between the number of trades and the absolute re-turn15 Naeligs and Skjeltorp (2006) argue that the number of trades is the crucialcomponent of trading volume Regressing Corr(N o f tradesit |Rit|) on our slopemeasure using a fixed-effects regression model we only find weak support for thenegative relationship between the volume-volatility relation and the slope of theorder book We compute the volume-volatility relation alternatively by directly in-corporating daily trading volume Corr(Trading Volumeit |Rit|) and estimate thefollowing regression model

Corr(Trading Volumeit |Rit|) = β0 + β1SLOPE100it + β2SLOPE10000it


Key Finding 6 Our results indicate that there is a significant amount of informationin the order book and the slope of the order book should be considered in theoreticalmodels trying to explain the cause of the volume-volatility relation We find that thevolume-volatility relation seems to be stronger when the slope of the order book issteeper which contradicts the results of Naeligs and Skjeltorp (2006)

The effect appears to be robust across model specifications and can be observed forboth slope measure specifications and increases in magnitude when controlling formarket capitalization Further the explanatory power in all models presented inTable 512 is approximately 60 and does not vary much between models It is note-worthy that the first and the fourth model already explain 6080 and 5900 of thevariation in the volume-volatility relation

555 A Note on Causality

While it is generally difficult to derive causality in economics it is especially chal-lenging in a market microstructure setting However some conjectures with respectto causality can be derived economically To further increase the robustness of ourresults we perform additional causality tests on our slope measure trading activityand volatility for different sub periods Granger causality test results are depicted inTable 513 and Table 514 We find that the null can be rejected at the 5 level for all

15We do not adjust for day-of-week effects as proposed by Naeligs and Skjeltorp 2006 as cryptocurren-cies can be traded at any time


TABLE 512 The Volume-Volatility relation and the Slope of the Order Book

Notes This table shows the fixed effects regression results for Equation 512 The dependent variableCorr(Trading Volumeit |Rit|) is the daily correlation coefficient measured over a month between USDtrading volume and the absolute return Coefficients of SLOPE100 and SLOPE10000 are multiplied by104 for better readability The coefficient of Spread and Trade size has been multiplied by 102

Model (1) (2) (3) (4) (5) (6)

SLOPE100 037 037 044

(368) (298) (357)

SLOPE10000 560 597 561

(190) (181) (190)

Spread 071 084 161 136

(089) (098) (210) (162)

Trade size minus003 011


ln(MCAP) minus002 007

(minus043) (214)



R2 6080 6150 6070 5900 6030 5910


tests performed on the granger-causal relationship between number of trades andthe slope of the order book (Table 513) indicating a bidirectional granger-causal re-lationship between the two variables16 Examining the causal relationship betweenvolatility and the slope of the order book (Table 514) reveals a less unambiguouspicture We can not reject bidirectional Granger causality between volatility andslope changes for Bitcoin at the 10 level and for Bitcoin Cash at the 1 level whilewe find evidence for unidirectional causality from volatility to slope changes forEthereum

16For most of the tests shown in Table 513 the null hypothesis can even be rejected at a 1 signifi-cance level


TABLE 513 Linear Granger Causality Test Results on Trades

Notes This table shows Granger causality test results between the number of trades and order bookslope changes Lag lengths are set with the Akaike (1974) information criterion adjusted by the numberof observations considering the first five lagged days Sig denotes the marginal significance level ofthe computed χ2-statistic used to test the zero restrictions implied by the null hypothesis of Grangernoncausality Slope indicates the number of ticks considered for estimating the order book slope mea-sure

H0 No of trades do not H0 Slope changes do notcause slope changes cause No of trades

Lags N Slope χ2 Sign χ2 Sign

Panel A Bitcoin (April 2018 ndash August 2019)

5 18 100 2077 000 2872 0005 18 10000 11883 000 2496 000

Panel B Bitcoin Cash (May 2018 ndash August 2019)

4 13 100 19617 000 1133 0024 13 10000 1315 001 4619 000

Panel C Ethereum (April 2018 ndash August 2019)

5 15 100 6630 000 1594 0015 15 10000 8031 000 1567 001


TABLE 514 Linear Granger Causality Test Results on Volatility

Notes This table shows Granger causality test results between volatility (|Rit|) and order book slopechanges Lag lengths are set with the Akaike (1974) information criterion adjusted by the number ofobservations considering the first five lagged days Sign denotes the marginal significance level of thecomputed χ2-statistic used to test the zero restrictions implied by the null hypothesis of Granger non-causality Slope indicates the number of ticks considered for estimating the order book slope measure

H0 Volatility does not H0 Slope changes do notcause slope changes cause volatility



5 22 100 1027 007 4196 0002 51 10000 1232 000 662 004


5 14 100 2550 000 23472 0005 14 10000 2254 000 1765 000


1 68 100 854 000 001 0751 68 10000 249 012 023 063


56 Conclusion

This study examines the statistical properties of cryptocurrency limit order booksOur descriptive analysis reveals that the secondary market for cryptocurrencies isshaped by high cancellation rates and a preference for limit orders over market or-ders These findings are valid for both the buy and sell side Our subsequent empir-ical analysis reveals six key findingsBuilding upon our in-depth analysis of the LOBs of three major cryptocurrencieswe find evidence supporting our hypothesis that a group of lazy investors dis-regard the full granularity of the price grid which is reflected in volume peaks atcertain price levels distant to the best price (Key Finding 1) Testing the empiri-cal fit of different explanatory models for the slope of the order book we find thata linear approximation of the slope of the order book is reasonable without losingmuch explanatory power (Key Finding 2) Employing three different models wegain empirical evidence on the interplay between the slope of the order book pricechanges and trading activity We compute two slope measures (the inner slope andthe deeper slope) different in the range of data used for estimation The empiricalanalysis further reveals that the inner slope can not explain return variation whilethe deeper slope seems to contain information about cryptocurrency returns (KeyFinding 3) This finding indicates that limit orders in the depths of the order bookndash even though having a low probability to be executed and not being mechanicallylinked to price changes ndash are still relevant for the price formation process This find-ing suggests that traders incorporate the whole state of the order book when buyingor selling cryptocurrencies (Key Finding 4) This explanation is also practically rea-sonable as the state of the limit order book is visible to traders at any given timeIn addition we find that the inner slope of the order book has a significant positiveeffect on trading activity However this relation changes sign when considering thedeeper slope of the order book (Key Finding 5) This phenomenon has also beenobserved in stock markets by Naeligs and Skjeltorp (2006) Our results point into thesame direction and show that this anomaly can be observed in cryptocurrency mar-kets as well Moreover we find a positive relationship between trading volume andvolatility confirming the volume-volatility to be prevalent in cryptocurrency mar-kets as well Surprisingly we find the relation to be weaker when the slope of theorder book is steeper (Key Finding 6) This finding is significant across six differ-ent model specifications and contradicts the empirical results presented by Naeligs andSkjeltorp (2006)We further find that the spread equals the minimum tick size of 001 USD in 4202of the time in our data raising questions about the perception of the endogeneity ofthe spread by traders in todayrsquos markets This issue emerges from the finite gran-ularity of the price grid and has also been discussed by Biais Hillion and Spatt(1995)

85

Chapter 6

Heuristics in Cryptocurrency LimitOrder Placement

Exchanges and trading platforms allow us to analyze the behavior of interactingmarket participants From a scientific point of view the main advantage of financialmarkets is that all market participants trade the same asset with the aim to max-imize profits incentivizing rational behavior This unique setting allows to studynot only economic and financial theories but also theories of human behavior Inthis study we show that a power-law used to describe the distribution of limit or-der prices in stock markets can be extended to cryptocurrency markets despite thevery different market frameworks We hypothesize that cryptocurrency traders fallback to heuristics when placing limit orders provide a straightforward model ex-tension that accounts for this behavior and show that our model fits the empiricaldata better than the vanilla power-law model proposed in the literature

61 Introduction

In this study we examine the probability of incoming orders in a limit order mar-ket Today most of security trading is arranged via electronic order matching by ex-changes operating a limit order book (LOB) The flow of incoming limit orders yieldsinsights into market dynamics at the fine-granular level and therefore receives par-ticular interest from academia Zovko and Farmer (2002) and Bouchaud Meacutezardand Potters (2002) find a striking behavioral pattern while observing the placementof limit orders in stock markets Bouchaud Meacutezard and Potters (2002) state thatthe distribution of incoming limit order prices depends on the distance towards thebest available price and can be described by a universal power-law Further studiesprovide empirical evidence that supports the validity of the proposed power-law forvarying stocks and time frames (see Potters and Bouchaud 2003 Mike and Farmer2008 and Cont Stoikov and Talreja 2010)We extend the current state of scientific knowledge by showing that the univer-sal validity of the power-law can be generally extended to cryptocurrency mar-kets Accompanied by the increasing popularity and adoption of cryptocurrencies

Chapter 6 Heuristics in Cryptocurrency Limit Order Placement 86

secondary markets for cryptocurrencies emerge Those trading platforms operateLOBs in the same way as traditional stock exchanges and similar trading rules ap-ply1 However there exist some considerable differences between traditional ex-changes and cryptocurrency trading platforms as well Cryptocurrency trading plat-forms typically operate globally and without a break while traditional (national) ex-changes only provide services during localized business hours Further traded vol-ume in stock markets is of magnitudes higher than cryptocurrency trading volumeand important characteristics of market participants likely differ as well betweenthese market places eg investment horizon trading strategy or location Thosemarket conditions raise doubts on the unconditional transferability of the power-law to cryptocurrency marketsUsing cryptocurrency limit order flow data from a major cryptocurrency tradingplatform we find that order placement is increased when the relative distance to-wards the best price equals an (positive) integer We explain our finding by tradersusing a heuristic when placing limit orders Supposing that traders reduce the com-plexity of order placement by not considering the full granularity of the price gridwe propose a straightforward extension of the power-law relation and show that theextended model fits the empirical distribution of incoming limit order prices Theappeal of this extension lies in itrsquos simplicity which reflects the simplification madeby traders during limit order placement The existence of a substantial amount oftraders that rely on a simple heuristic when placing limit orders might indicate thatthe cryptocurrency market is still an emerging market where inefficiencies exist Toour knowledge no previous study that focuses on stock markets detects this place-ment behavior The remainder of this investigation is structured as follows Section62 provides the theoretical background of the proposed power-law in limit ordermarkets We motivate our hypothesis in Section 63 We derive our theoretical modeland provide empirical results in Section 64 Section 65 concludes

62 Statistics and Distribution of Incoming Limit Order Prices

In a limit order market traders submit a limit order to buy or sell a quantity ofan asset for a specific price Limit orders at the highest bid or lowest sell price arematched against incoming market orders while other limit orders remain in the LOBuntil they are either executed when the current price reaches their price level at alater point in time or canceled by the trader Similar to Bouchaud Meacutezard andPotters (2002) we denote ∆ as the absolute difference measured in ticks on the USDollar price grid between the current best price and the price of an incoming limitorder

∆ = |best available priceminus limit order price|1Notably most cryptocurrency trading platforms operate a LOW that follows a first-come first

serve principle and a price-time priority


Note that ∆ can be computed for bid and ask limit orders similarly An impatienttrader chooses ∆ to be close to zero thereby increasing the likelihood of quick orderexecution2 However the time advantage of placing a limit order close to the cur-rent bid or ask price runs in opposition to the risk of having the order executed at anunfavorable price Hence each trader faces a trade-off when placing a limit orderZovko and Farmer (2002) state that the choice of placing a limit order also dependson the individual goal of a trader and his trading strategy making order placement acomplex task Based on the assumption that traders differ in their expectations of fu-ture returns time horizon and risk aversion Chiarella Iori and Perelloacute (2009) showthat heterogeneous trading rules impact the limit order flow Hence the distributionof incoming limit order prices is not a priori clearBy analyzing stock LOBs from the Paris Bourse Bouchaud Meacutezard and Potters(2002) detect that the probability of a limit order arriving at ∆ can be described by auniversal power-law of the following form3

ρ(∆) sim 1∆1+micro

The advantage of power-law distributions lies in their simple representation andtheir prevalence in real world data Power-laws can be found across a wide rangeof complex economic relations that are influenced by many independent factors Infact the well-known pareto distribution (Pareto 1964) which is widely applied ineconomics is a power-lawUsing stock order flow data of three listed stocks Bouchaud Meacutezard and Potters(2002) estimate that micro = 06 While subsequent studies validate the power-law dis-tribution disagreement persists about the true value of micro

63 Limit Order Placement

We gather meta data by tracking the limit order flow via the application program-ming interface of a large cryptocurrency exchange Our data originates from fourcryptocurrencies traded against the US Dollar (USD) in April and June 2018 namelyBitcoin (BTC) Ethereum (ETH) Bitcoin Cash (BCH) and Litecoin (LTC)4 In totalwe gather 9 625 526 BTCUSD 34 905 938 ETHUSD 17 827 541 BCHUSD and33 467 649 LTCUSD limit order prices with a maximum of 500 ticks away fromthe current best price Supplemental information about the cryptocurrencies in oursample is given in Table 61 Figure 61 shows the empirical distribution of incoming

2∆ = 0 implies a limit order at the best bid price or ask price3Note that the proposed power-law is scale-invariant ie if ∆ is multiplied by a constant c we

would derive the following direct proportionality ρ(c∆) = 1(c∆)1+micro = cminus(1+micro)ρ(∆) prop ρ(∆) where prop

denotes direct proportionality4BTC ETH BCH and LTC combined account for roughly 80 of the total cryptocurrency market

capitalization


TABLE 61 Descriptive Statistics

Notes This table contains some supplemental information about the four cryptocurrency pairsBTCUSD ETHUSD BCHUSD and LTCUSD in our sample

BTCUSD ETHUSD BCHUSD LTCUSD

Initial price (USD) 818601 52435 126254 14092Final price (USD) 618003 41399 66551 7949Tick size (USD) 001 001 001 001Avg daily transaction volume (thsd) 931 BTC 10721 ETH 2104 BCH 20225 LTCAvg daily transaction volume (USD mn) 7763 6723 2090 1998Total limit orders1 (mn) 963 3491 1783 3347

limit ask orders (mn) 516 1955 973 1799 limit bid orders (mn) 446 1535 810 1548

1 Numbers only consider limit orders with ∆ le 500 that were recorded from April to June 2018 (with gaps)

limit order prices dependent on the distance towards the best price (∆) in logarith-mized form up to a maximum distance of ∆max = 500 for BTC ETH BCH and LTCThe depicted values are aggregated across the bid and ask sideFrom visual inspection the power-law seems to fit the distribution of limit orderprices quite well considering incoming BTCUSD and LTCUSD limit orders How-ever the distribution of incoming limit orders for ETHUSD reveals a deviatingpicture and shows that while most orders are placed close to the best price a localmaximum exists at about 100 to 200 ticks away from the current best price Thislocal maximum can be observed in the BCHUSD order placement as well and iseven more pronounced5 Nevertheless the observed cryptocurrencies consistentlyshow that the probability of order placement diminishes when moving away fromthe best price as implied by the power-law leading to our first hypothesis

Hypothesis A (H0A) The distribution of incoming limit order prices in cryptocur-rency markets follows a power-law like the order flow in stock markets

Analyzing Figure 61 reveals that the distribution of incoming limit orders exhibitspeaks which can be observed across all four currencies to a varying degree Theyare most eminent in the limit order flow of the BTCUSD currency pair We sup-pose that these peaks do not occur at random but follow a simple rule originating ina tradersrsquo heuristics used to reduce the complexity of the order placement decisionprocess which leads to our second hypothesis

Hypothesis B (H0B) The probability of an incoming limit order is increased whenthe distance towards the best price (∆) divided by 100 is a positive integer

5While it is not the focus of this study we suppose that the humps are related to the liquidity of therespective asset


Note that an incoming limit order at ∆ = 100 refers to a limit order placed at exactly100 USD away from the best price To illustrate our hypothesis we include verticallines in Figure 61 highlighting the associated values for ∆ = [100 200 500] Wefind the respective vertical lines to be located exactly at the peaks of the distributionacross all observed cryptocurrencies

64 Empirical Results

To empirically fit the power-law proposed by Bouchaud Meacutezard and Potters (2002)and in order to empirically test itrsquos universal character by applying it in cryptocur-rency markets (H0A) we estimate the value of the parameter micro We do so by trans-forming the proposed power-law equation by taking the natural logarithm at bothsides

P(∆) =c

∆1+micro

rArr ln (P(∆))︸︷︷︸Y

= ln(c)︸︷︷︸β0

+ (minus1minus micro)︸︷︷︸β1

middot ln(∆)︸︷︷︸X1

P(∆) denotes the frequency of a limit buy or limit sell order arriving ∆ ticks awayfrom the current best price As indicated by the brackets the transformed equationcan be substituted to derive an equation that is linear in its parameters Using themethod of ordinary least squares this property allows us to compute an estimatorfor micro by estimating the value of β1 as micro = minusβ1 minus 1Motivated by our graphical analysis of order placement behavior (Figure 61) andH0B we extend this model by including a factor λ and a function DN(∆) that de-pends on the value of ∆ and has the value 1 if ∆

100 isin N and zero otherwise Wepropose the following expression describing the distribution of incoming limit or-der prices

P(∆) =c middot eλmiddotDN(∆)

∆1+micro


= ln(c)︸︷︷︸β0



+ λ︸︷︷︸β2

middotDN(∆)︸︷︷︸X2

As shown above the extended model can be transformed by applying the logarithmat both sides as well allowing us to estimate its parameters analog to the vanillapower-law model We measure DN(∆) by creating a dummy variable indicatingwhether the distance of an incoming limit order price divided by 100 is a naturalnumber ie whether the price of an incoming limit order i is 1 2 5 USD awayfrom the best price ε denotes the error term Our final empirical model is shown in


Formula 61

ln(P(∆))i = β0 + β1ln(∆)i + β2DN(∆)i + εi where (61)

DN(∆) =

1 i f ∆

100 isinN

0 else

Our empirical results are shown in Table 62 We find β1 and micro respectively to be

TABLE 62 Fitted Power Law Results

Notes This table provides regression results for Equation 61 Note that we estimate micro by computingmicro = minus(β1 + 1) To capture potential measurement errors we allow a narrow interval of [Nminus 005 N+005] for which the dummy variable DN = 1 Data was recorded from April to June 2018 (with gaps)The exponent of the denominator of the power-law is denoted as 1 + micro ie a negative value gt minus1 formicro is not surprising

Currency(USD) BTC BTC ETH ETH BCH BCH LTC LTC

micro -066 -065 -026 -025 -078 -077 085 085(-2670) (-2895) (-3142) (-3182) (-688) (-706) (-5577) (-5600)

DN(∆) 031 023 022 025(796) (304) (203) (236)

Constant yes yes yes yes yes yes yes yesAdj R2 5880 6338 6640 6694 849 906 8617 8630N 500 500 500 500 500 500 500 500

p lt 010 p lt 005 p lt 001 t-statistics in parenthesis

statistically significant at the 1-level across all cryptocurrencies and model spec-ifications indicating that the postulated universal nature of the power-law can beextended to cryptocurrency markets as well thereby strongly boosting itrsquos externalvalidity

Interim Result A Therefore we can not reject H0A

The explanatory power of the ordinary power-law of up to 8617 (LTCUSD) forcryptocurrencies indicates that the power-law may represent a fundamental charac-teristic of competitive limit order markets and should hence be considered an asset-independent feature in the fields of market microstructure As cryptocurrency mar-kets did not yet exist when the power-law distribution was first discovered ourfindings provide empirical evidence that the power-law distribution is an inherentproperty of competitive marketsMoreover our results show that the estimated parameter micro varies between minus078 to085 which is considerably below the estimated value in stock markets ranging from06 to 15 (see Bouchaud Meacutezard and Potters 2002 and Zovko and Farmer 2002)


A low value for micro implies that more limit orders are placed away from the currentprice in cryptocurrency markets This finding suggests that cryptocurrency tradersexpect larger USD price jumps in cryptocurrency markets which would be in linewith the general perception of cryptocurrencies being more volatile than stocksWe further find that the coefficient λ of our constructed variable DN(∆) is statisti-cally significant and of the same magnitude across all four cryptocurrencies

Interim Result B Hence we can not reject H0B

The results show that the probability of a specific limit order price is higher when thelimit order price is exactly 100 200 500 USD away from the current best priceWe suggest that this occurrence is of behavioral nature and traders prefer roundednumbers when setting the price level of limit orders ie they follow heuristics whenplacing limit orders Obviously traders seem to base their decision on the relativedistance towards the best price rather than the best price itself Eg our results sug-gest that a trader does not contemplate about buying at 50 USD but rather aboutbuying at 5 USD below the current price6 Including DN in our regression modelalso increases the explanatory power by 013 to 458 percentage points

Key Finding We argue that while the simple form of the power-law is appealing inexplaining the limit order price distribution the existence of behavioral preferencesof traders makes it necessary to account for specific values of the relative distancetowards the best price in future theoretical models

To our knowledge the existence of peaks in order price frequencies has not beenobserved by studies focusing on highly efficient stock marketsFigure 62 compares the fit of the universal power-law proposed by Bouchaud Meacutez-ard and Potters (2002) with the fit of our extended model exemplary for incomingBTCUSD limit orders Figure 62 demonstrates that solely using the power-law todescribe the distribution of limit order prices leads to a slight underestimation ofP(∆) ndash especially close to the best price ndash which can be reduced by accounting forpeaks

65 Conclusion

In this study we examine the limit order placement of four major cryptocurrenciesand compute the relative distance of incoming limit order prices towards the bestprice at arrival We show that limit order placement in cryptocurrency markets can

6Preferences of financial agents for rounded numbers have been documented in the literature be-fore eg Corwin (2003) finds that the issue yield in seasoned equity offers is related to underwriterpricing conventions such as price rounding and pricing relative to the bid quote Further links betweennumeric fluency and human preferences have been documented by Kettle and Haumlubl (2010)


be described by a power-law that was first discovered in the stock market literatureFurther we find empirical evidence that traders prefer integers when consideringhow far from the best price they place their limit order This preference for integersis likely the result of applied heuristics during limit order placement Traders resortto heuristics when dealing with the complexity of limit order placement and reducethe complexity by disregarding the full granularity of the price gridWe suggest a straightforward extension of the power-law approach to describe thedistribution of limit order prices and show that accounting for the observed behav-ioral regularity yields a better empirical fit than the plain power-law approach sug-gested in the literature Given the statistical significance of our finding we suggestthat future models trying to explain limit order placement should take this behav-ioral regularity into accountFurther studies are necessary to better understand the factors that influence the limitorder placement process in cryptocurrency markets over time Our data gives hintsto the fact that the observed phenomenon of an increased probability for incominglimit orders at certain numbers might be scale-invariant in that more incoming limitorders are placed at 01 USD than at 011 USD (05 USD than at 049 USD) away fromthe best price However our extended model needs to be tested in other markets andtime frames We would expect that it can show an improved explanatory power es-pecially in illiquid and emerging markets Finally the occurrence of humps in thelimit order price distribution in our data indicates a potential link between limit or-der prices and liquidity This complex relation needs further research and is crucialto fully understand and describe order placement behavior


0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

14

15

16

log(P(∆

))

Fitted Power Law micro=-066

Left to right log(100) log(200) log(500)

BTCUSD

0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

log(P(∆

))



BCHUSD

0 1 2 3 4 5 6 7

log(∆)

9

10

11

12

13

14

15

log(P(∆

))



ETHUSD

Notes This figure shows the cumulative distribution of ∆ of incoming orders as a function of ∆ forBTCUSD BCHUSD ETHUSD and LTCUSD We take the logarithm at both sides ∆ le 500

FIGURE 61 Incoming Limit Orders and Fitted Power-Law


0 1 2 3 4 5 6 7

log(∆)

4

6

8

10

12

14

16

18

20

log(P(∆

))

Fitted Power Law micro=085


LTCUSD

FIGURE 61 (continued)

1000

030

000

5000

070

000

0 100 200 300 400 500Delta

BTCUSD Fitted Power-LawFitted Power-Law (Extended Model)

P(D

elta

)

Notes We set an interval of [Nminus 001 N + 001] for which the dummy variable DN = 1 Note thedecreasing magnitude of the peaks in the extended model caused by the parameter λ which also fitsthe empirical data The predicted peaks decrease because λ is incorporated multiplicative rather thanadditive in the extended model We estimate λ as the β-coefficient of the dummy variable DN = 1

FIGURE 62 Fitted Power-Law and Extended Model for BTCUSD

95

Chapter 7

ReaktionenderKryptowaumlhrungsmaumlrkteaufdieCOVID-19-Pandemie

Chapter 7 has been published as a journal articleHaumlfner David Jianan He and Dirk Schiereck (2020) Reaktionen der Kryptowaumlh-rungsmaumlrkte auf die COVID-19-Pandemie In Corporate Finance 05-06 pp 141ndash144ISSN 2198-8889

96

Chapter 8

Concluding Remarks

This dissertation explores the behavior and interaction of market participants in dif-ferent market conditions and is focused on empirically analyzing the efficiency ofvarious financial market segments and the behavior of market participants undercompetition and their reaction to exogenous shocks and imperfect informationIn Chapter 2 we examine the development of corporate social responsibility andits compatibility with efficient investments By reviewing 74 leading articles pub-lished between 1984 and 2016 we provide a review on the scientific literature andhighlight the importance and effects of socially responsible and sustainable invest-ments from three different perspectives the investor level the company level andthe portfolio level We provide an in-depth analysis of the motivation behavior anddemographics of socially responsible investors We further show that the motivationof the management financial inducement and exogenous influence ndash eg publicityor consumer behavior ndash can impact the extent to which companies act responsiblePortfolio implications are focused on the financial effects ie risk and return of so-cially responsible investments We group the existing literature and empirical find-ings geographically as the regulatory framework and government support is likelyto shape the investment environment eg Henke (2016) finds that European fundshave higher responsibility scores We discuss the historic development of sociallyresponsible investments and gather empirical results considering the financial per-formance of socially responsible investment funds over time It remains an openquestion whether responsible investment funds perform better or worse than unre-stricted funds as evidence for both directions is provided in the literature Howeverwe do not find clear evidence that social responsible investments struggle which ndashconsidering the restricted investment universe ndash is quite remarkable However fur-ther empirical studies including more recent data are necessary to determine theimpact of social responsibility on financial performanceIn Chapter 3 we empirically analyze the impact of a regulatory change in Euro-pean financial markets on information asymmetry idiosyncratic risk and liquidityThe introduction of MiFID II in January 2018 included extensive changes for finan-cial markets with the superordinate aim to increase transparency The most drasticchange concerns the market for investment research provided by financial analysts

Chapter 8 Concluding Remarks 97

We discuss how the literature defines the role of analysts in financial markets andhighlight how investment research is conducted in the EU prior to MiFID II andcompare the regulatory framework with the US Using data of 1281 EU firms and1646 US firms we employ a difference-in-difference approach to determine the ef-fect of MiFID II on a firmrsquos analyst coverage liquidity stock trading volume thebid-ask spread and its idiosyncratic risk We empirically show that MiFID II had asignificant impact on European financial markets as the overall bid-ask spread andthe idiosyncratic risk increased We hypothesize that a financial analyst experiencesincreased competition since the implementation of MiFID II which should have averifiable effect on the bid-ask spread We provide empirical evidence that an in-crease in analyst coverage reduces the bid-ask spread of a stock emphasizing theinformational role of financial analysts By estimating the effect of analyst cover-age on the bid-ask spread (while controlling for liquidity) we find evidence thatfinancial analysts affect the level of asymmetric information of a stock This effect isamplified by MiFID IIIn Chapter 4 we empirically analyze the structure and performance of ICOs Weshow that ICOs share many similarities with IPOs and are currently generally per-formed by young and entrepreneurial companies due to their low direct costs Gath-ering data from 175 ICOs we empirically analyze the indirect costs of an ICO bycomputing the underpricing during ICOs We find that the underpricing phenome-non ndash defined as a positive return at the first trading day of a stock ndash can be observedduring an ICO as well and is even more apparent than during IPOs There existseveral explanatory approaches for the existence of IPO underpricing of which themajority is centered on information asymmetry between the issuing firm the under-writing bank and investors We discuss these hypotheses and their transferabilityonto the ICO setting Our results suggest that the high level of ICO underpricing isrelated to a higher degree of information asymmetry between the issuing firm andinvestors or between investors as information about ICOs is scarce oftentimes witha white paper as the sole source of information Therefore investors might demandunderpricing as compensation for participating in an ICO with little prior knowl-edge about the risk and return profile However we cannot rule out behavioralexplanatory approaches and further research is necessary to fully understand thecause for the high level of underpricing observed during ICOs We further find thatthe long-term performance of new cryptocurrencies generally remains below theperformance of established cryptocurrencies This anomaly has been documentedin the stock markets as well (see Ritter and Welch 2002)


In Chapter 5 we analyze statistical properties of cryptocurrency limit order booksFirst we motivate the importance of market microstructure as a prerequisite for ef-ficient capital allocation Next we discuss the scope of application for cryptocurren-cies and their disruptive potential We gather data from one of the largest cryptocur-rency exchanges and reconstruct the limit order books for three different cryptocur-rencies (Bitcoin Ethereum and Bitcoin Cash) We find two distinctive features of theaggregated limit order book volume known as the slope of the order book The slopevaries over time and can have linear concave or even convex properties We test theempirical fit for different shapes of the slope of the order book and find that a linearslope is generally a sufficient approximation and justifiable with respect to modelsimplicity We further stumble upon an anomaly in the distribution of limit orderbook volume We observe volume peaks in the limit order book at specific pricelevels relative to the best price This anomaly is detectable across all analyzed limitorder books We hypothesize that this pattern is created by a group of investorswhich do not consider the entire granularity of the price grid This leads to an accu-mulation of available volume at specific price levels We coin this the ldquolazy investorhypothesisrdquo and develop a straight-forward regression model to test the empiricalsignificance of this finding and find the respective estimated regression coefficientto be highly significant at both the bid and the ask side across all limit order booksFinally we test whether there is information stored in the order book slope Similarempirical work has been done by Naeligs and Skjeltorp (2006) who study stock limit or-der books We investigate three groups of models using the slope of the order bookto explain price changes trading volume and the correlation between price changesand trading volume We compute the slope on a daily basis averaged across the bidand ask side of the limit order book and show that the slope of the limit order bookcan help to explain variation in the dependent variables Our findings also indicatethat limit orders placed far away from the best price still appear to be relevant forthe price formation process This finding is interesting as there is no plausible me-chanical link explaining this relationshipIn Chapter 6 we focus on the limit order placement behavior of cryptocurrencytraders There have been some seemingly universal statistical laws discovered inthe stock market literature that describe the placement of limit orders Each traderhas to decide at which price level he or she issues a limit order Each trader faces atrade-off regarding the execution probability of a limit order time-priority and thethreat of having the limit order executed at an unfavorable price The decision on theprice level further depends on the individual trading strategy liquidity preferencesand other factors making limit order placement a complex process This makes theemergence of patterns in the arrival of limit orders even more surprising Zovko andFarmer (2002) and Bouchaud Meacutezard and Potters (2002) show that the probabilityof an incoming limit order can be described by a universal power-law in stock mar-kets We document that this power-law can be observed in cryptocurrency markets


as well Moreover we find empirical evidence that cryptocurrency traders seem toprefer certain price levels and more limit orders are issued at price levels exactly100 200 500 USD away from the best price We hypothesize that the probabil-ity of an incoming limit order is increased when the distance towards the best pricedivided by 100 is an integer indicating that traders apply heuristics when placinglimit orders We extend the power-law model to account for this occurrence andshow that our extended model better fits the empirical data than the plain modelIn Chapter 7 we analyze the impact of a radical change of the macroeconomic en-vironment on the market for cryptocurrencies The outbreak of the coronavirusin 20192020 had a sudden impact on all industries and caused unprecedencedchanges to the market environment across all asset classes around the globe Inthis chapter we focus on the impact on the cryptocurrency market and the poten-tial of cryptocurrencies as a save investment haven in times of a crisis We find thatcryptocurrencies with a high market capitalization seem to be affected by the overalldepreciation at the beginning of the outbreak and ndash in line with Corbet et al (2020)ndash their suitability as a save investment haven is therefore questioned Howeversmaller cryptocurrencies seem to be affected less by the virus outbreak and mightbe more appropriate for a crisis-resistant save haven portfolio However the fullextent of COVID-19 and its impact on cryptocurrency markets is not yet foreseeableand further research needs to be conducted to understand the ways in which a virusoutbreak affects this emerging asset classAfter completion of this thesis our findings concerning the six broader researchquestions derived in Chapter 1 can be summarized as follows


Result Market efficiency is compatible with social responsibility mainly dueto non-monetary benefits (eg consumer reactions personal values or pub-licity) Further empirically evidence suggests that funds engaging in sociallyresponsible investments do not perform significantly worse than unrestrictedfunds


Result The net positive effect of regulation on financial markets remains anopen question and side effects of a regulatory change are tough to predict Inour empirical analysis however we specifically focus on MiFID II and showthat this regulatory change led to an increase in the average bid-ask spreadand idiosyncratic risk across EU stock markets We also find that the effect of


analyst coverage on the bid-ask spread is enhanced since the implementationof MiFID II


Result While investors do not have the capabilities to obtain all price-relevantinformation they do not seem to be deterred from information asymmetryThey rather seek compensation for engaging in uncertain and thereby riskyinvestments We show that ICOs ndash which are typically surrounded by a highlevel of information asymmetry between investors and the issuing company ndashexhibit high initial returns at the first trading day We compare our results tothe IPO process and find that ICO underpricing is higher than IPO underpric-ing supporting the hypothesis that the underpricing is caused by informationasymmetry and can be regarded as a compensation for investors


Result We group market dynamics into three distinct categories Price volatil-ity trading volume and the volume-volatility relation We try to connect themarket microstructure and the market development by computing daily aver-ages of certain microstructure features Analyzing the market for cryptocur-rencies we find that the slope of the order book ndash representing the aggregatedlimit order book volume ndash has an impact on all three of the categories Themarket microstructure seems to significantly impact the broader market devel-opment and should be considered in asset pricing models Traders can observethe state of the limit order book at any given point in time and seem to con-sider its shape during their investment decision


Result We find that limit order placement is a complex task and the solu-tion is determined by individual time and price preferences and the respectivetrading strategy We find empirical evidence suggesting that traders resort toheuristics when determining limit order prices By disregarding the granular-ity of the price grid traders reduce the complexity and find a solution moreeasily This behavioral regularity has not been documented in the literaturebefore and we provide an extended model that takes this finding into account


Our empirical results show that our theoretical model can explain the distri-bution of incoming limit order prices better than existing models


Result We find that smaller cryptocurrencies exhibit return patterns that sep-arate them from cryptocurrencies with a high market capitalization Tradingvolume seems to be a predictor for returns of small cryptocurrencies This rela-tion is enhanced since the exogenous shock caused by COVID-19 Further theinteraction term between return and trading volume has a significant negativeeffect on future returns of large cryptocurrencies While all cryptocurrenciessuffered from the initial macroeconomic shock caused by the COVID-19 pan-demic the market for smaller cryptocurrencies seems to be more resilient toexogenous shocks

In conclusion this dissertation provides an in-depth analysis of the complex inter-play between financial market agents in different market settings We find that infor-mation asymmetry plays a crucial role across markets time periods and geograph-ical regions and provide empirical evidence about the extent to which informationdisparity can affect asset prices and liquidity Throughout this dissertation we showthat investors act responsible despite no direct monetary incentive (Chapter 2) relyon financial analysts to obtain price-relevant information and are affected by regu-latory changes (Chapter 3) demand a premium when not having full informationduring an investment decision (Chapter 4) use the limit order book as a source ofinformation (Chapter 5) and resort to heuristics when solving complex tasks (Chap-ter 6) Chapter 7 finally showcases how sensitive the market equilibrium can beto a sudden change in the market setting and discusses the adjustments made byinvestors

102

Bibliography

Akaike Hirotugu (1974) ldquoA new look at the statistical model identificationrdquo InIEEE transactions on automatic control 196 pp 716ndash723

AMF (2018) MiFID II Impact of the new tick size regime URL httpswwwamf-franceorgen_USPublicationsLettres-et-cahiersRisques-et-tendances

ArchivesdocId=workspace5C3A5C2F5C2FSpacesStore5C2F4ee6cbf6-

c425-4537-ab74-ef249b9d316d (visited on 01152019)Amihud Yakov (2002) ldquoIlliquidity and stock returns cross-section and time-series

effectsrdquo In Journal of Financial Markets 51 pp 31ndash56Barber Brad Reuven Lehavy Maureen McNichols and Brett Trueman (2001) ldquoCan

investors profit from the prophets Security analyst recommendations and stockreturnsrdquo In The Journal of Finance 562 pp 531ndash563

Bessler Wolfgang and Matthias Stanzel (2007) ldquoQualitaumlt und Effizienz der Gewinn-prognosen von Analystenrdquo In Kredit und Kapital 401 pp 89ndash129

Biais Bruno Pierre Hillion and Chester Spatt (1995) ldquoAn empirical analysis of thelimit order book and the order flow in the Paris Bourserdquo In The Journal of Finance505 pp 1655ndash1689

Bouchaud Jean-Philippe Marc Meacutezard and Marc Potters (2002) ldquoStatistical proper-ties of stock order books empirical results and modelsrdquo In Quantitative Finance24 pp 251ndash256

Casey Jean-Pierre and Karel Lannoo (2009) The MiFID Revolution Cambridge Uni-versity Press

Chen Qi Itay Goldstein and Wei Jiang (2006) ldquoPrice informativeness and invest-ment sensitivity to stock pricerdquo In The Review of Financial Studies 203 pp 619ndash650

Chiarella Carl Giulia Iori and Josep Perelloacute (2009) ldquoThe impact of heterogeneoustrading rules on the limit order book and order flowsrdquo In Journal of EconomicDynamics and Control 333 pp 525ndash537

Chung Kee H Thomas H McInish Robert A Wood and Donald J Wyhowski(1995) ldquoProduction of information information asymmetry and the bid-askspread Empirical evidence from analystsrsquo forecastsrdquo In Journal of Banking amp Fi-nance 196 pp 1025ndash1046

Citigate Dewe Rogerson (2019) 11th Annual IR Survey URL httpscitigate5C- dewe5C- rogersoncomuk- companies- among- hardest- hit- decline-

Bibliography 103

analyst-coverage-according-citigate-dewe-rogersons-11th-annual-ir-

survey (visited on 10282019)Cont Rama (2001) ldquoEmpirical properties of asset returns stylized facts and statisti-

cal issuesrdquo In Quantitative Finance 12 pp 223ndash236Cont Rama Sasha Stoikov and Rishi Talreja (2010) ldquoA stochastic model for order

book dynamicsrdquo In Operations Research 583 pp 549ndash563Copeland Thomas E and Dan Galai (1983) ldquoInformation effects on the bid-ask

spreadrdquo In The Journal of Finance 385 pp 1457ndash1469Corbet Shaen Charles Larkin and Brian Lucey (2020) ldquoThe contagion effects of

the covid-19 pandemic Evidence from gold and cryptocurrenciesrdquo In FinanceResearch Letters 35 p 101554

Corwin Shane A (2003) ldquoThe determinants of underpricing for seasoned equityoffersrdquo In The Journal of Finance 585 pp 2249ndash2279

De Bondt Werner F M and Richard H Thaler (1990) ldquoDo security analysts overre-actrdquo In The American Economic Review pp 52ndash57

De Vries Alex (2018) ldquoBitcoinrsquos growing energy problemrdquo In Joule 25 pp 801ndash805Deutsche Boumlrse (2017) Die Auswirkungen von MiFID II auf die Verfuumlgbarkeit von Re-

search URL httpswwwdeutsche- boersecomresourceblob166524ab0a3455fe3aab17051126128607e577dataMarket-Trends-MiFID-II-Research

pdf (visited on 11042019)Deutscher Investor Relations Verband (2017) Moumlgliche Auswirkungen von MiFID II

fuumlr Emittenten URL httpswwwdirkorgdirk_webseitestaticuploads171006-MV_KS_Praesentation_Bommer_MiFIDpdf (visited on 11042019)

Durnev Artyom Randall Morck Bernard Yeung and Paul Zarowin (2003) ldquoDoesgreater firm-specific return variation mean more or less informed stock pricingrdquoIn Journal of Accounting Research 415 pp 797ndash836

Easley David Nicholas M Kiefer Maureen OrsquoHara and Joseph B Paperman (1996)ldquoLiquidity information and infrequently traded stocksrdquo In The Journal of Finance514 pp 1405ndash1436

Easley David Maureen OrsquoHara and Joseph Paperman (1998) ldquoFinancial analystsand information-based traderdquo In Journal of Financial Markets 12 pp 175ndash201

ESMA (2018) Amendment to Commission Delegated Regulation (EU) 2017588 (RTS 11)URL httpswwwesmaeuropaeupress-newsconsultationsamendment-commission- delegated- regulation- eu- 2017588- rts- 11TODO (visited on01152019)

European Commission (2014) ldquoDirective 201465EU of the European Parliamentand of the Council of 15 May 2014 on Market Financial Instruments and Amend-ing Directive 200292EC and Directive 201161EUrdquo In Official Journal of theEuropean Union 173 pp 349ndash496

Fama Eugene F (1970) ldquoEfficient capital markets A review of theory and empiricalworkrdquo In The Journal of Finance 252 pp 383ndash417

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Fang Bingxu Ole-Kristian Hope Zhongwei Huang and Rucsandra Moldovan (2019)ldquoThe Effects of MiFID II on Sell-Side Analysts Buy-Side Analysts and FirmsrdquoURL httpspapersssrncomsol3paperscfmabstract_id=3422155(visited on 11042019)

Ferrarini Guido and Eddy Wymeersch (2006) Investor protection in Europe corporatelaw making the MiFID and beyond Oxford University Press

Foster Andrew J (1995) ldquoVolume-volatility relationships for crude oil futures mar-ketsrdquo In Journal of Futures Markets 158 pp 929ndash951

Foster F Douglas and S Viswanathan (1995) ldquoCan speculative trading explain thevolumendashvolatility relationrdquo In Journal of Business amp Economic Statistics 134pp 379ndash396

French Kenneth R and Richard Roll (1986) ldquoStock return variances The arrival ofinformation and the reaction of tradersrdquo In Journal of Financial Economics 171pp 5ndash26

Fung Hung-Gay and Gary A Patterson (1999) ldquoThe dynamic relationship of volatil-ity volume and market depth in currency futures marketsrdquo In Journal of Inter-national Financial Markets Institutions and Money 91 pp 33ndash59

Givoly Dan and Josef Lakonishok (1979) ldquoThe information content of financial ana-lystsrsquo forecasts of earnings Some evidence on semi-strong inefficiencyrdquo In Jour-nal of Accounting and Economics 13 pp 165ndash185

Gleason Cristi A and Charles M C Lee (2003) ldquoAnalyst forecast revisions and mar-ket price discoveryrdquo In The Accounting Review 781 pp 193ndash225

Glosten Lawrence R (1987) ldquoComponents of the bid-ask spread and the statisticalproperties of transaction pricesrdquo In The Journal of Finance 425 pp 1293ndash1307

Glosten Lawrence R and Lawrence E Harris (1988) ldquoEstimating the components ofthe bidask spreadrdquo In Journal of Financial Economics 211 pp 123ndash142

Glosten Lawrence R and Paul R Milgrom (1985) ldquoBid ask and transaction pricesin a specialist market with heterogeneously informed tradersrdquo In Journal of Fi-nancial Economics 141 pp 71ndash100

Grossman Sanford J and Joseph E Stiglitz (1980) ldquoOn the impossibility of informa-tionally efficient marketsrdquo In The American Economic Review 703 pp 393ndash408

Henke Hans-Martin (2016) ldquoThe effect of social screening on bond mutual fundperformancerdquo In Journal of Banking amp Finance 67 pp 69ndash84

Holste Bjoern and Christoph Gallus (2019) Sind Krypto-Waumlhrungsmaumlrkte Fair(AreCrypto-Currency Markets Fair) Working Paper

Johnsen Edward J and John Grady (2017) SEC provides relief to US firms attemptingto comply with EU MiFID IIrsquos research ldquounbundling provisions URL httpswwwdlapipercomenusinsightspublications201710sec-relief-to-us-

firms-attempting-to-comply (visited on 01102019)

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Karpoff Jonathan M (1987) ldquoThe relation between price changes and trading vol-ume A surveyrdquo In Journal of Financial and Quantitative Analysis 221 pp 109ndash126

Kelly Bryan and Alexander Ljungqvist (2012) ldquoTesting asymmetric-information as-set pricing modelsrdquo In The Review of Financial Studies 255 pp 1366ndash1413

Kettle Keri and Gerald Haumlubl (2010) ldquoNumeric fluency and preferencerdquo In NA ndashNorth American Advances 37 pp 150ndash152

Kim Oliver and Robert E Verrecchia (1994) ldquoMarket liquidity and volume aroundearnings announcementsrdquo In Journal of Accounting and Economics 171-2 pp 41ndash67

Lippiatt Todd and Michael Oved (2018) The Two Token Waterfall URL httpsframeworkfactoraio (visited on 02072019)

Mantegna Rosario N and H Eugene Stanley (1999) Introduction to econophysics cor-relations and complexity in finance Cambridge University Press

Marshall Ben R Nhut H Nguyen and Nuttawat Visaltanachoti (2019) Bitcoin liq-uidity Working Paper Massey University and Auckland University of Technol-ogy

Mike Szabolcs and J Doyne Farmer (2008) ldquoAn empirical behavioral model of liq-uidity and volatilityrdquo In Journal of Economic Dynamics and Control 321 pp 200ndash234

Morck Randall Bernard Yeung and Wayne Yu (2000) ldquoThe information content ofstock markets why do emerging markets have synchronous stock price move-mentsrdquo In Journal of Financial Economics 581-2 pp 215ndash260

Naeligs Randi and Johannes A Skjeltorp (2006) ldquoOrder book characteristics and thevolumendashvolatility relation Empirical evidence from a limit order marketrdquo InJournal of Financial Markets 94 pp 408ndash432

Nakamoto Satoshi (2008) Bitcoin A peer-to-peer electronic cash system Working PaperNofer Michael Peter Gomber Oliver Hinz and Dirk Schiereck (2017) ldquoBlockchainrdquo

In Business amp Information Systems Engineering 593 pp 183ndash187OrsquoHara Maureen (1997) Market microstructure theory WileyOrsquoHara Maureen (2015) ldquoHigh frequency market microstructurerdquo In Journal of Fi-

nancial Economics 1162 pp 257ndash270Pareto Vilfredo (1964) Cours drsquoeacuteconomie politique Vol 1 Librairie DrozPiotroski Joseph D and Darren T Roulstone (2004) ldquoThe influence of analysts

institutional investors and insiders on the incorporation of market industryand firm-specific information into stock pricesrdquo In The Accounting Review 794pp 1119ndash1151

Potters Marc and Jean-Philippe Bouchaud (2003) ldquoMore statistical properties of or-der books and price impactrdquo In Physica A Statistical Mechanics and its Applica-tions 3241-2 pp 133ndash140

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Preece Rhodri (2019) MiFID II One Year On URL httpswwwcfainstituteorg-mediadocumentssurveycfa-mifid-II-survey-reportashx (visitedon 10292019)

Preis Tobias Sebastian Golke Wolfgang Paul and Johannes J Schneider (2006) ldquoMulti-agent-based order book model of financial marketsrdquo In EPL (Europhysics Letters)753 pp 510ndash516

Ritter Jay R (1987) ldquoThe costs of going publicrdquo In Journal of Financial Economics192 pp 269ndash281

Ritter Jay R and Ivo Welch (2002) ldquoA review of IPO activity pricing and alloca-tionsrdquo In The Journal of Finance 574 pp 1795ndash1828

Roll Richard (1988) ldquoR2rdquo In The Journal of Finance 433 pp 541ndash566Sarwar Ghulam (2003) ldquoThe interrelation of price volatility and trading volume

of currency optionsrdquo In Journal of Futures Markets Futures Options and OtherDerivative Products 237 pp 681ndash700

SEC (2017) Response of the chief counselrsquos office division of investment management URLhttpswwwsecgovdivisionsinvestmentnoaction2017sifma-102617-

202ahtm (visited on 01142019)Stoll Hans R (1989) ldquoInferring the components of the bid-ask spread Theory and

empirical testsrdquo In The Journal of Finance 441 pp 115ndash134Thomsen Steen and Frederik Vinten (2014) ldquoDelistings and the costs of governance

a study of European stock exchanges 1996ndash2004rdquo In Journal of Management ampGovernance 183 pp 793ndash833

Wallmeier Martin (2005) ldquoAnalystsrsquo earnings forecasts for DAX100 firms during thestock market boom of the 1990srdquo In Financial Markets and Portfolio Management192 pp 131ndash151

Wolfson Rachel (2018) A first for Manhattan $30M Real Estate Property Tokenized WithBlockchain URL httpswwwforbescomsitesrachelwolfson20181003a- first- for- manhattan- 30m- real- estate- property- tokenized- with-

blockchain1910a2d48957 (visited on 02072019)Womack Kent L (1996) ldquoDo brokerage analystsrsquo recommendations have investment

valuerdquo In The Journal of Finance 511 pp 137ndash167Zhu PengCheng Vijay Jog and Isaac Otchere (2014) ldquoIdiosyncratic volatility and

mergers and acquisitions in emerging marketsrdquo In Emerging Markets Review 19pp 18ndash48

Zovko Ilija and J Doyne Farmer (2002) ldquoThe power of patience a behavioural reg-ularity in limit-order placementrdquo In Quantitative Finance 25 pp 387ndash392

Abstract

Acknowledgements

Synopsis

Motivation

Thesis Structure

What do we know about socially responsible investments

The Role of Investment Research in view of MiFID II An Empirical Analysis of Information Asymmetry Idiosyncratic Risk and Liquidity

Introduction

Regulation of Investment Research

Investment Research in the European Union

Investment Research in the United States

The Role of Investment Research in Capital Markets

Variable Definition

Illiquidity

The Bid-Ask Spread

Stock Price Informativeness and Idiosyncratic Risk

Data

Descriptive Statistics

Empirical Analysis

Difference-in-Differences Approach

Multiple Regression on the Bid-Ask Spread

Multiple Regression on Idiosyncratic Risk

Summary Statistics on Medium-Term Effects on Research Coverage

Conclusion

Innovative Finanzierungen uumlber Initial Coin Offerings Struktur und bisherige Performance

Statistical Properties of Cryptocurrency Order Books

Introduction

Structure of a Limit Order Book

Data Collection and Description

Data Acquisition Process


Limit Order Book Characteristics in Cryptocurrency Markets

The Shape of the Average Order Book

The Bitcoin Order Book

The Bitcoin Cash Order Book

The Ethereum Order Book

The Lazy Investor Hypothesis

Convexity of the Order Book Slope

Informativeness of Order Book Characteristics

Measuring the Slope of the Limit Order Book

Order Book Slope and Volatility

Order Book Slope and Trading Activity

Order Book Slope and the Volume-Volatility Relation

A Note on Causality

Conclusion

Heuristics in Cryptocurrency Limit Order Placement

Introduction

Statistics and Distribution of Incoming Limit Order Prices

Limit Order Placement

Empirical Results

Conclusion

Reaktionen der Kryptowaumlhrungsmaumlrkte auf die COVID-19-Pandemie

Concluding Remarks

Bibliography

i

Haumlfner David Market Efficiency Behavior and Information Asymmetry EmpiricalEvidence from Cryptocurrency and Stock MarketsDarmstadt Technische Universitaumlt DarmstadtTag der Einreichung 02072020Tag der muumlndlichen Pruumlfung 15122020URN urnnbndetuda-tuprints-174953Jahr der Veroumlffentlichung der Dissertation auf TUprints 2021Veroumlffentlicht unter CC BY-NC-ND 40 Internationalhttpscreativecommonsorglicenses

ii


Sir Isaac Newton

iii




Dr rer pol


by David HAumlFNER


iv



v


vi

Contents

Abstract iii

Acknowledgements v











vii





56 Conclusion 84




Bibliography 102

viii

List of Figures














ix

List of Tables







x



xi


xii


1

Chapter 1

Synopsis

11 Motivation















12 Thesis Structure



















analysts






8

Chapter 2



9

Chapter 3



31 Introduction



10



11







12




13






14




15




16



17





18


341 Illiquidity



VOLit(31)



19








(33)



20







21




SYNCHi = ln(

R2i

1minus R2i

)(34)


Rt = α + βRmt + εt (35)




22


Ψi = ln(

1minus R2i

R2i

)(36)


35 Data




23






24






25






26








27










28


Ψi = ln

(1minus R2

i

R2i

)(37)




i )R2i ) R2



j = 50

implies Ψj = 0





29







30











31





32








(310)



33





34


37 Conclusion



35



36






Total 248936 10000


37










t-statistic


t-statistic


t-statistic


t-statistic
















38








(3791) (3612) (1402) (805)


(1026) (minus103) (minus2388) (2127)



Control Variables


(128) (minus232) (329) (2498)


(823) (381) (437) (655)


(21319) (11015) (12855) (13415)



Observations 241598 80558 80277 80763R2 310 283 309 298



39











(minus1289) (099) (135) (minus820)

Control Variables


(6799) (3127) (852) (1792)




(864) (1333) (1132) (969)


(25037) (13739) (5984) (5036)

Observations 242170 80742 80578 80850R2 215 162 206 264



40






Total 1271 10000 389 10000

41

Chapter 4



42

Chapter 5


51 Introduction












-

-

-

-

-

-

-

-

-

-

-

MinimumTick Size

Demand(Bid)

Supply(Ask)

Bid-AskSpread

b(t)

a(t)

Price

















A Interest

A Principal

A Excess B Excess

B Principal
















































Panel A Sell Side

Received

Limit 9958 9976 9991 9975

Market 042 024 009 025

Done

Canceled 9836 9844 9881 9854

Filled 164 156 119 146

Panel B Buy Side

Received

Limit 9967 9979 9968 9971

Market 033 021 032 029

Done

Canceled 9922 9936 9877 9912

Filled 078 064 123 088


TAB

LE

54

Con

diti

onal

Prob

abili

ties

ofth

eO

rder

Type

inTi

me

tgiv

enth

eO

rder

Type

intminus

1

Not

eTh

eta

ble

show

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eco

ndit

iona

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sof

spec

ific

orde

rty

pes

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win

gea

chot

her

inth

eor

der

book

Eac

hco

lum

nre

pres

ents

the

prob

abili

tyof

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resp

ecti

veor

der

type

give

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eor

der

type

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epr

evio

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der

equa

lsth

eor

der

type

ofth

ero

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heta

ble

can

bere

adas

follo

ws

The

cond

itio

nalp

roba

bilit

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ata

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lBuy

Ord

eris

follo

wed

bya

Larg

eBu

yO

rder

is

Pro

b(L

arge

Buy t|S

mal

lBuy

tminus1)

=0

22

Th

esu

mof

each

row

equa

ls10

0

Not

eth

atth

esu

mof

each

colu

mn

does

not

give

the

unco

ndit

iona

lpro

babi

lity

ofth

ere

spec

tive

even

tSi

mila

rto

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sH

illio

nan

dSp

att(

1995

)w

ede

fine

mar

keto

rder

sas

larg

eif

thei

rvo

lum

eex

ceed

sth

eex

isti

ngvo

lum

eat

the

best

bid

oras

kpr

ice

and

the

pric

eal

low

sth

eor

der

tocl

imb

thro

ugh

the

oppo

site

side

ofth

eor

der

book

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allm

arke

tord

erdo

esno

tpos

sess

atle

asto

neof

thos

ech

arac

teri

stic

s

Tim

etminus

1Ti

me

tLa

rge

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lBu

y

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erin

side

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erbe

low

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cele

dBu

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all

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erat

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ove

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cele

dSe

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rder

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eBu

y1

13

258

12

86

213

14

43

354

50

06

010

2

13

045

15

11

135

8Sm

allB

uy0

22

258

4

09

224

21

76

332

40

05

029

0

72

169

12

60

205

2Bu

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rder

insi

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read

005

0

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676

5

68

174

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001

0

04

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0

47

720

9

29

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ad0

06

030

5

31

636

17

63

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50

01

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0

94

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63

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belo

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read

003

0

23

143

1

06

256

139

75

003

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14

089

0

90

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414

80

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003

0

16

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1

86

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53

002

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41

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ll0

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019

2

59

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18

67

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43

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12

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160

8

67

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allS

ell

004

0

48

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10

182

824

51

073

5

53

827

2

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425

23

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erin

side

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ad0

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3

92

079

13

93

162

90

07

014

5

95

409

12

70

420

0Se

llO

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atSp

read

001

0

25

110

1

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023

37

005

0

26

303

5

99

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631

30

Sell

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ove

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ad0

04

024

1

12

118

24

18

220

40

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0

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117

24

87

240

3C

ance

led

Sell

Ord

er0

02

015

1

27

104

13

67

256

80

02

011

2

98

181

26

72

265

4











Day 1Day 11


05

10152025


me (in BTC)

ΔDay 1

Day 11

Day 21

Day 31

Day 41

Day 51

05

10152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observa

tion Day

Aggrega

ted Ask

Volume

(in BTC

)

Δ







Day 1Day 11

Day 21Day 31

Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation D

ay

Bid Volume (

in BTC)

ΔDay 1

Day 11Day 21

Day 31Day 41

Day 51

0510152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observatio

n Day

Aggregated

Bid Volum

e (in BTC)

Δ







∆










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9262 8892 9885 9598 8653 9743

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9774 8263 9888 9700 8667 9786

p lt 010 p lt 005 p lt 001







∆




4

8

12

16

20

24

28

32


in BCH)


50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation Day

Aggregated Ask Vo

lume (in BCH)




2

4

6

8

10

12

14

16

18

20

22

24


e (in BCH)


0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observatio

n Day

Aggregated

Bid Volum

e (in BCH)










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 15178 15177 15178 101 100 101

R2 9574 8871 9992 9933 8198 9633

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 002 005


N 15178 15177 15178 101 100 101

R2 9822 8450 9947 9943 7864 9436

p lt 010 p lt 005 p lt 001




Day 1Day 6


020406080

100120140160180

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observati

on Day

Ask Volum

e (in ETH

)


05001000150020002500


ted Ask Volume

(in ETH)






020406080100120140


me (in ETH)


05001000150020002500



)Δ










∆







I∆ =

1 i f ∆ = 100 mod(0)0 else






Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 018 071


N 35395 35394 35395 101 100 101

R2 9861 8122 9844 9928 7496 9214

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 002




Std Err 046 0


N 35395 35394 35395 101 100 101

R2 9981 7718 9712 9849 7191 8995

p lt 010 p lt 005 p lt 001









I∆ 028 035 158 151 811 901

Std Err 0 0 004 003 008 136



N 50412 50412 15177 15177 35394 35394

R2 3105 600 1205 1606 2358 279

p lt 010 p lt 005 p lt 001























Ask Side



β-Coeff 064 086 104 017 074 118

Std Err 0 0 0 001 0 001


N 50412 15177 14 35394 100 100 100

R2 9985 9994 9994 8853 9982 9950


Bid Side



β-Coeff 065 087 108 013 059 109

Std Err 0 0 0 001 001 001


N 50412 15177 35394 100 100 100

R2 9971 9996 9999 7166 9926 9950


p lt 010 p lt 005 p lt 001






sumi=1

(1N

N

sumn=1

VOLinasktj

)(53)





SLOPEask =



(55)





2(56)




Volatilityit =








N o f Tradesit(59)














Model (1) (2) (3) (4) (5) (6)

SLOPE100 047 036 040

(265) (157) (148)

SLOPE10000 952 943 950

(172) (171) (166)

N of trades 004 004 005 005

(386) (361) (520) (410)

Trade size 073 071 071 073

(404) (382) (405) (390)

ln(MCAP) -287 273

(minus031) (035)

Spread -163 -319




R2 1120 2920 2920 960 2930 2940















Model (1) (2) (3) (4)

SLOPE100 1203 416

(873) (223)

SLOPE10000 6792 -6736

(143) (minus171)

Trade size -4395 -12390


ln(MCAP) 2416250 3267030

(403) (677)

Spread 453150 553640

(441) (545)



R2 5520 6340 3650 6290














Model (1) (2) (3) (4) (5) (6)

SLOPE100 037 037 044

(368) (298) (357)

SLOPE10000 560 597 561

(190) (181) (190)

Spread 071 084 161 136

(089) (098) (210) (162)




(minus043) (214)



R2 6080 6150 6070 5900 6030 5910










5 18 100 2077 000 2872 0005 18 10000 11883 000 2496 000


4 13 100 19617 000 1133 0024 13 10000 1315 001 4619 000


5 15 100 6630 000 1594 0015 15 10000 8031 000 1567 001







5 22 100 1027 007 4196 0002 51 10000 1232 000 662 004


5 14 100 2550 000 23472 0005 14 10000 2254 000 1765 000


1 68 100 854 000 001 0751 68 10000 249 012 023 063


56 Conclusion


85

Chapter 6



61 Introduction

















capitalization

















P(∆) =c

∆1+micro


= ln(c)︸︷︷︸β0






∆1+micro


= ln(c)︸︷︷︸β0



+ λ︸︷︷︸β2




Formula 61


DN(∆) =

1 i f ∆

100 isinN

0 else





micro -066 -065 -026 -025 -078 -077 085 085(-2670) (-2895) (-3142) (-3182) (-688) (-706) (-5577) (-5600)

DN(∆) 031 023 022 025(796) (304) (203) (236)












65 Conclusion






0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

14

15

16

log(P(∆

))



BTCUSD

0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

log(P(∆

))



BCHUSD

0 1 2 3 4 5 6 7

log(∆)

9

10

11

12

13

14

15

log(P(∆

))



ETHUSD




0 1 2 3 4 5 6 7

log(∆)

4

6

8

10

12

14

16

18

20

log(P(∆

))



LTCUSD


1000

030

000

5000

070

000

0 100 200 300 400 500Delta


P(D

elta

)



95

Chapter 7



96

Chapter 8

Concluding Remarks

























102

Bibliography















Bibliography 103



















Bibliography 104

















Bibliography 105
















Bibliography 106

















Abstract

Acknowledgements

Synopsis

Motivation

Thesis Structure



Introduction





Variable Definition

Illiquidity

The Bid-Ask Spread


Data


Empirical Analysis





Conclusion



Introduction

















A Note on Causality

Conclusion


Introduction



Empirical Results

Conclusion


Concluding Remarks

Bibliography

ii


Sir Isaac Newton

iii




Dr rer pol


by David HAumlFNER


iv



v


vi

Contents

Abstract iii

Acknowledgements v











vii





56 Conclusion 84




Bibliography 102

viii

List of Figures














ix

List of Tables







x



xi


xii


1

Chapter 1

Synopsis

11 Motivation















12 Thesis Structure



















analysts






8

Chapter 2



9

Chapter 3



31 Introduction



10



11







12




13






14




15




16



17





18


341 Illiquidity



VOLit(31)



19








(33)



20







21




SYNCHi = ln(

R2i

1minus R2i

)(34)


Rt = α + βRmt + εt (35)




22


Ψi = ln(

1minus R2i

R2i

)(36)


35 Data




23






24






25






26








27










28


Ψi = ln

(1minus R2

i

R2i

)(37)




i )R2i ) R2



j = 50

implies Ψj = 0





29







30











31





32








(310)



33





34


37 Conclusion



35



36






Total 248936 10000


37










t-statistic


t-statistic


t-statistic


t-statistic
















38








(3791) (3612) (1402) (805)


(1026) (minus103) (minus2388) (2127)



Control Variables


(128) (minus232) (329) (2498)


(823) (381) (437) (655)


(21319) (11015) (12855) (13415)



Observations 241598 80558 80277 80763R2 310 283 309 298



39











(minus1289) (099) (135) (minus820)

Control Variables


(6799) (3127) (852) (1792)




(864) (1333) (1132) (969)


(25037) (13739) (5984) (5036)

Observations 242170 80742 80578 80850R2 215 162 206 264



40






Total 1271 10000 389 10000

41

Chapter 4



42

Chapter 5


51 Introduction












-

-

-

-

-

-

-

-

-

-

-

MinimumTick Size

Demand(Bid)

Supply(Ask)

Bid-AskSpread

b(t)

a(t)

Price

















A Interest

A Principal

A Excess B Excess

B Principal
















































Panel A Sell Side

Received

Limit 9958 9976 9991 9975

Market 042 024 009 025

Done

Canceled 9836 9844 9881 9854

Filled 164 156 119 146

Panel B Buy Side

Received

Limit 9967 9979 9968 9971

Market 033 021 032 029

Done

Canceled 9922 9936 9877 9912

Filled 078 064 123 088


TAB

LE

54

Con

diti

onal

Prob

abili

ties

ofth

eO

rder

Type

inTi

me

tgiv

enth

eO

rder

Type

intminus

1

Not

eTh

eta

ble

show

sth

eco

ndit

iona

lpro

babi

litie

sof

spec

ific

orde

rty

pes

follo

win

gea

chot

her

inth

eor

der

book

Eac

hco

lum

nre

pres

ents

the

prob

abili

tyof

the

resp

ecti

veor

der

type

give

nth

eor

der

type

ofth

epr

evio

usor

der

equa

lsth

eor

der

type

ofth

ero

wT

heta

ble

can

bere

adas

follo

ws

The

cond

itio

nalp

roba

bilit

yth

ata

Smal

lBuy

Ord

eris

follo

wed

bya

Larg

eBu

yO

rder

is

Pro

b(L

arge

Buy t|S

mal

lBuy

tminus1)

=0

22

Th

esu

mof

each

row

equa

ls10

0

Not

eth

atth

esu

mof

each

colu

mn

does

not

give

the

unco

ndit

iona

lpro

babi

lity

ofth

ere

spec

tive

even

tSi

mila

rto

Biai

sH

illio

nan

dSp

att(

1995

)w

ede

fine

mar

keto

rder

sas

larg

eif

thei

rvo

lum

eex

ceed

sth

eex

isti

ngvo

lum

eat

the

best

bid

oras

kpr

ice

and

the

pric

eal

low

sth

eor

der

tocl

imb

thro

ugh

the

oppo

site

side

ofth

eor

der

book

Asm

allm

arke

tord

erdo

esno

tpos

sess

atle

asto

neof

thos

ech

arac

teri

stic

s

Tim

etminus

1Ti

me

tLa

rge

Buy

Smal

lBu

y

Buy

Ord

erin

side

Spre

ad

Buy

Ord

erat

Spre

ad

Buy

Ord

erbe

low

Spre

ad

Can

cele

dBu

yO

rder

Larg

eSe

llSm

all

Sell

Sell

Ord

erin

side

Spre

ad

Sell

Ord

erat

Spre

ad

Sell

Ord

erab

ove

Spre

ad

Can

cele

dSe

llO

rder

Larg

eBu

y1

13

258

12

86

213

14

43

354

50

06

010

2

13

045

15

11

135

8Sm

allB

uy0

22

258

4

09

224

21

76

332

40

05

029

0

72

169

12

60

205

2Bu

yO

rder

insi

deSp

read

005

0

11

676

5

68

174

550

58

001

0

04

235

0

47

720

9

29

Buy

Ord

erat

Spre

ad0

06

030

5

31

636

17

63

387

50

01

014

0

94

136

12

63

165

1Bu

yO

rder

belo

wSp

read

003

0

23

143

1

06

256

139

75

003

0

14

089

0

90

151

414

80

Can

cele

dBu

yO

rder

003

0

16

384

1

86

385

027

53

002

0

09

095

0

79

981

16

41

Larg

eSe

ll0

07

019

2

59

035

18

67

135

43

54

835

12

91

160

8

67

295

1Sm

allS

ell

004

0

48

070

2

10

182

824

51

073

5

53

827

2

20

119

425

23

Sell

Ord

erin

side

Spre

ad0

02

009

3

92

079

13

93

162

90

07

014

5

95

409

12

70

420

0Se

llO

rder

atSp

read

001

0

25

110

1

60

184

023

37

005

0

26

303

5

99

146

631

30

Sell

Ord

erab

ove

Spre

ad0

04

024

1

12

118

24

18

220

40

03

016

0

93

117

24

87

240

3C

ance

led

Sell

Ord

er0

02

015

1

27

104

13

67

256

80

02

011

2

98

181

26

72

265

4











Day 1Day 11


05

10152025


me (in BTC)

ΔDay 1

Day 11

Day 21

Day 31

Day 41

Day 51

05

10152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observa

tion Day

Aggrega

ted Ask

Volume

(in BTC

)

Δ







Day 1Day 11

Day 21Day 31

Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation D

ay

Bid Volume (

in BTC)

ΔDay 1

Day 11Day 21

Day 31Day 41

Day 51

0510152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observatio

n Day

Aggregated

Bid Volum

e (in BTC)

Δ







∆










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9262 8892 9885 9598 8653 9743

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9774 8263 9888 9700 8667 9786

p lt 010 p lt 005 p lt 001







∆




4

8

12

16

20

24

28

32


in BCH)


50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation Day

Aggregated Ask Vo

lume (in BCH)




2

4

6

8

10

12

14

16

18

20

22

24


e (in BCH)


0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observatio

n Day

Aggregated

Bid Volum

e (in BCH)










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 15178 15177 15178 101 100 101

R2 9574 8871 9992 9933 8198 9633

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 002 005


N 15178 15177 15178 101 100 101

R2 9822 8450 9947 9943 7864 9436

p lt 010 p lt 005 p lt 001




Day 1Day 6


020406080

100120140160180

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observati

on Day

Ask Volum

e (in ETH

)


05001000150020002500


ted Ask Volume

(in ETH)






020406080100120140


me (in ETH)


05001000150020002500



)Δ










∆







I∆ =

1 i f ∆ = 100 mod(0)0 else






Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 018 071


N 35395 35394 35395 101 100 101

R2 9861 8122 9844 9928 7496 9214

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 002




Std Err 046 0


N 35395 35394 35395 101 100 101

R2 9981 7718 9712 9849 7191 8995

p lt 010 p lt 005 p lt 001









I∆ 028 035 158 151 811 901

Std Err 0 0 004 003 008 136



N 50412 50412 15177 15177 35394 35394

R2 3105 600 1205 1606 2358 279

p lt 010 p lt 005 p lt 001























Ask Side



β-Coeff 064 086 104 017 074 118

Std Err 0 0 0 001 0 001


N 50412 15177 14 35394 100 100 100

R2 9985 9994 9994 8853 9982 9950


Bid Side



β-Coeff 065 087 108 013 059 109

Std Err 0 0 0 001 001 001


N 50412 15177 35394 100 100 100

R2 9971 9996 9999 7166 9926 9950


p lt 010 p lt 005 p lt 001






sumi=1

(1N

N

sumn=1

VOLinasktj

)(53)





SLOPEask =



(55)





2(56)




Volatilityit =








N o f Tradesit(59)














Model (1) (2) (3) (4) (5) (6)

SLOPE100 047 036 040

(265) (157) (148)

SLOPE10000 952 943 950

(172) (171) (166)

N of trades 004 004 005 005

(386) (361) (520) (410)

Trade size 073 071 071 073

(404) (382) (405) (390)

ln(MCAP) -287 273

(minus031) (035)

Spread -163 -319




R2 1120 2920 2920 960 2930 2940















Model (1) (2) (3) (4)

SLOPE100 1203 416

(873) (223)

SLOPE10000 6792 -6736

(143) (minus171)

Trade size -4395 -12390


ln(MCAP) 2416250 3267030

(403) (677)

Spread 453150 553640

(441) (545)



R2 5520 6340 3650 6290














Model (1) (2) (3) (4) (5) (6)

SLOPE100 037 037 044

(368) (298) (357)

SLOPE10000 560 597 561

(190) (181) (190)

Spread 071 084 161 136

(089) (098) (210) (162)




(minus043) (214)



R2 6080 6150 6070 5900 6030 5910










5 18 100 2077 000 2872 0005 18 10000 11883 000 2496 000


4 13 100 19617 000 1133 0024 13 10000 1315 001 4619 000


5 15 100 6630 000 1594 0015 15 10000 8031 000 1567 001







5 22 100 1027 007 4196 0002 51 10000 1232 000 662 004


5 14 100 2550 000 23472 0005 14 10000 2254 000 1765 000


1 68 100 854 000 001 0751 68 10000 249 012 023 063


56 Conclusion


85

Chapter 6



61 Introduction

















capitalization

















P(∆) =c

∆1+micro


= ln(c)︸︷︷︸β0






∆1+micro


= ln(c)︸︷︷︸β0



+ λ︸︷︷︸β2




Formula 61


DN(∆) =

1 i f ∆

100 isinN

0 else





micro -066 -065 -026 -025 -078 -077 085 085(-2670) (-2895) (-3142) (-3182) (-688) (-706) (-5577) (-5600)

DN(∆) 031 023 022 025(796) (304) (203) (236)












65 Conclusion






0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

14

15

16

log(P(∆

))



BTCUSD

0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

log(P(∆

))



BCHUSD

0 1 2 3 4 5 6 7

log(∆)

9

10

11

12

13

14

15

log(P(∆

))



ETHUSD




0 1 2 3 4 5 6 7

log(∆)

4

6

8

10

12

14

16

18

20

log(P(∆

))



LTCUSD


1000

030

000

5000

070

000

0 100 200 300 400 500Delta


P(D

elta

)



95

Chapter 7



96

Chapter 8

Concluding Remarks

























102

Bibliography















Bibliography 103



















Bibliography 104

















Bibliography 105
















Bibliography 106

















Abstract

Acknowledgements

Synopsis

Motivation

Thesis Structure



Introduction





Variable Definition

Illiquidity

The Bid-Ask Spread


Data


Empirical Analysis





Conclusion



Introduction

















A Note on Causality

Conclusion


Introduction



Empirical Results

Conclusion


Concluding Remarks

Bibliography

iii




Dr rer pol


by David HAumlFNER


iv



v


vi

Contents

Abstract iii

Acknowledgements v











vii





56 Conclusion 84




Bibliography 102

viii

List of Figures














ix

List of Tables







x



xi


xii


1

Chapter 1

Synopsis

11 Motivation















12 Thesis Structure



















analysts






8

Chapter 2



9

Chapter 3



31 Introduction



10



11







12




13






14




15




16



17





18


341 Illiquidity



VOLit(31)



19








(33)



20







21




SYNCHi = ln(

R2i

1minus R2i

)(34)


Rt = α + βRmt + εt (35)




22


Ψi = ln(

1minus R2i

R2i

)(36)


35 Data




23






24






25






26








27










28


Ψi = ln

(1minus R2

i

R2i

)(37)




i )R2i ) R2



j = 50

implies Ψj = 0





29







30











31





32








(310)



33





34


37 Conclusion



35



36






Total 248936 10000


37










t-statistic


t-statistic


t-statistic


t-statistic
















38








(3791) (3612) (1402) (805)


(1026) (minus103) (minus2388) (2127)



Control Variables


(128) (minus232) (329) (2498)


(823) (381) (437) (655)


(21319) (11015) (12855) (13415)



Observations 241598 80558 80277 80763R2 310 283 309 298



39











(minus1289) (099) (135) (minus820)

Control Variables


(6799) (3127) (852) (1792)




(864) (1333) (1132) (969)


(25037) (13739) (5984) (5036)

Observations 242170 80742 80578 80850R2 215 162 206 264



40






Total 1271 10000 389 10000

41

Chapter 4



42

Chapter 5


51 Introduction












-

-

-

-

-

-

-

-

-

-

-

MinimumTick Size

Demand(Bid)

Supply(Ask)

Bid-AskSpread

b(t)

a(t)

Price

















A Interest

A Principal

A Excess B Excess

B Principal
















































Panel A Sell Side

Received

Limit 9958 9976 9991 9975

Market 042 024 009 025

Done

Canceled 9836 9844 9881 9854

Filled 164 156 119 146

Panel B Buy Side

Received

Limit 9967 9979 9968 9971

Market 033 021 032 029

Done

Canceled 9922 9936 9877 9912

Filled 078 064 123 088


TAB

LE

54

Con

diti

onal

Prob

abili

ties

ofth

eO

rder

Type

inTi

me

tgiv

enth

eO

rder

Type

intminus

1

Not

eTh

eta

ble

show

sth

eco

ndit

iona

lpro

babi

litie

sof

spec

ific

orde

rty

pes

follo

win

gea

chot

her

inth

eor

der

book

Eac

hco

lum

nre

pres

ents

the

prob

abili

tyof

the

resp

ecti

veor

der

type

give

nth

eor

der

type

ofth

epr

evio

usor

der

equa

lsth

eor

der

type

ofth

ero

wT

heta

ble

can

bere

adas

follo

ws

The

cond

itio

nalp

roba

bilit

yth

ata

Smal

lBuy

Ord

eris

follo

wed

bya

Larg

eBu

yO

rder

is

Pro

b(L

arge

Buy t|S

mal

lBuy

tminus1)

=0

22

Th

esu

mof

each

row

equa

ls10

0

Not

eth

atth

esu

mof

each

colu

mn

does

not

give

the

unco

ndit

iona

lpro

babi

lity

ofth

ere

spec

tive

even

tSi

mila

rto

Biai

sH

illio

nan

dSp

att(

1995

)w

ede

fine

mar

keto

rder

sas

larg

eif

thei

rvo

lum

eex

ceed

sth

eex

isti

ngvo

lum

eat

the

best

bid

oras

kpr

ice

and

the

pric

eal

low

sth

eor

der

tocl

imb

thro

ugh

the

oppo

site

side

ofth

eor

der

book

Asm

allm

arke

tord

erdo

esno

tpos

sess

atle

asto

neof

thos

ech

arac

teri

stic

s

Tim

etminus

1Ti

me

tLa

rge

Buy

Smal

lBu

y

Buy

Ord

erin

side

Spre

ad

Buy

Ord

erat

Spre

ad

Buy

Ord

erbe

low

Spre

ad

Can

cele

dBu

yO

rder

Larg

eSe

llSm

all

Sell

Sell

Ord

erin

side

Spre

ad

Sell

Ord

erat

Spre

ad

Sell

Ord

erab

ove

Spre

ad

Can

cele

dSe

llO

rder

Larg

eBu

y1

13

258

12

86

213

14

43

354

50

06

010

2

13

045

15

11

135

8Sm

allB

uy0

22

258

4

09

224

21

76

332

40

05

029

0

72

169

12

60

205

2Bu

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rder

insi

deSp

read

005

0

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676

5

68

174

550

58

001

0

04

235

0

47

720

9

29

Buy

Ord

erat

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ad0

06

030

5

31

636

17

63

387

50

01

014

0

94

136

12

63

165

1Bu

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rder

belo

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read

003

0

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143

1

06

256

139

75

003

0

14

089

0

90

151

414

80

Can

cele

dBu

yO

rder

003

0

16

384

1

86

385

027

53

002

0

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095

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981

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41

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ll0

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035

18

67

135

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295

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ell

004

0

48

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2

10

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51

073

5

53

827

2

20

119

425

23

Sell

Ord

erin

side

Spre

ad0

02

009

3

92

079

13

93

162

90

07

014

5

95

409

12

70

420

0Se

llO

rder

atSp

read

001

0

25

110

1

60

184

023

37

005

0

26

303

5

99

146

631

30

Sell

Ord

erab

ove

Spre

ad0

04

024

1

12

118

24

18

220

40

03

016

0

93

117

24

87

240

3C

ance

led

Sell

Ord

er0

02

015

1

27

104

13

67

256

80

02

011

2

98

181

26

72

265

4











Day 1Day 11


05

10152025


me (in BTC)

ΔDay 1

Day 11

Day 21

Day 31

Day 41

Day 51

05

10152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observa

tion Day

Aggrega

ted Ask

Volume

(in BTC

)

Δ







Day 1Day 11

Day 21Day 31

Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation D

ay

Bid Volume (

in BTC)

ΔDay 1

Day 11Day 21

Day 31Day 41

Day 51

0510152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observatio

n Day

Aggregated

Bid Volum

e (in BTC)

Δ







∆










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9262 8892 9885 9598 8653 9743

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9774 8263 9888 9700 8667 9786

p lt 010 p lt 005 p lt 001







∆




4

8

12

16

20

24

28

32


in BCH)


50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation Day

Aggregated Ask Vo

lume (in BCH)




2

4

6

8

10

12

14

16

18

20

22

24


e (in BCH)


0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observatio

n Day

Aggregated

Bid Volum

e (in BCH)










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 15178 15177 15178 101 100 101

R2 9574 8871 9992 9933 8198 9633

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 002 005


N 15178 15177 15178 101 100 101

R2 9822 8450 9947 9943 7864 9436

p lt 010 p lt 005 p lt 001




Day 1Day 6


020406080

100120140160180

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observati

on Day

Ask Volum

e (in ETH

)


05001000150020002500


ted Ask Volume

(in ETH)






020406080100120140


me (in ETH)


05001000150020002500



)Δ










∆







I∆ =

1 i f ∆ = 100 mod(0)0 else






Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 018 071


N 35395 35394 35395 101 100 101

R2 9861 8122 9844 9928 7496 9214

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 002




Std Err 046 0


N 35395 35394 35395 101 100 101

R2 9981 7718 9712 9849 7191 8995

p lt 010 p lt 005 p lt 001









I∆ 028 035 158 151 811 901

Std Err 0 0 004 003 008 136



N 50412 50412 15177 15177 35394 35394

R2 3105 600 1205 1606 2358 279

p lt 010 p lt 005 p lt 001























Ask Side



β-Coeff 064 086 104 017 074 118

Std Err 0 0 0 001 0 001


N 50412 15177 14 35394 100 100 100

R2 9985 9994 9994 8853 9982 9950


Bid Side



β-Coeff 065 087 108 013 059 109

Std Err 0 0 0 001 001 001


N 50412 15177 35394 100 100 100

R2 9971 9996 9999 7166 9926 9950


p lt 010 p lt 005 p lt 001






sumi=1

(1N

N

sumn=1

VOLinasktj

)(53)





SLOPEask =



(55)





2(56)




Volatilityit =








N o f Tradesit(59)














Model (1) (2) (3) (4) (5) (6)

SLOPE100 047 036 040

(265) (157) (148)

SLOPE10000 952 943 950

(172) (171) (166)

N of trades 004 004 005 005

(386) (361) (520) (410)

Trade size 073 071 071 073

(404) (382) (405) (390)

ln(MCAP) -287 273

(minus031) (035)

Spread -163 -319




R2 1120 2920 2920 960 2930 2940















Model (1) (2) (3) (4)

SLOPE100 1203 416

(873) (223)

SLOPE10000 6792 -6736

(143) (minus171)

Trade size -4395 -12390


ln(MCAP) 2416250 3267030

(403) (677)

Spread 453150 553640

(441) (545)



R2 5520 6340 3650 6290














Model (1) (2) (3) (4) (5) (6)

SLOPE100 037 037 044

(368) (298) (357)

SLOPE10000 560 597 561

(190) (181) (190)

Spread 071 084 161 136

(089) (098) (210) (162)




(minus043) (214)



R2 6080 6150 6070 5900 6030 5910










5 18 100 2077 000 2872 0005 18 10000 11883 000 2496 000


4 13 100 19617 000 1133 0024 13 10000 1315 001 4619 000


5 15 100 6630 000 1594 0015 15 10000 8031 000 1567 001







5 22 100 1027 007 4196 0002 51 10000 1232 000 662 004


5 14 100 2550 000 23472 0005 14 10000 2254 000 1765 000


1 68 100 854 000 001 0751 68 10000 249 012 023 063


56 Conclusion


85

Chapter 6



61 Introduction

















capitalization

















P(∆) =c

∆1+micro


= ln(c)︸︷︷︸β0






∆1+micro


= ln(c)︸︷︷︸β0



+ λ︸︷︷︸β2




Formula 61


DN(∆) =

1 i f ∆

100 isinN

0 else





micro -066 -065 -026 -025 -078 -077 085 085(-2670) (-2895) (-3142) (-3182) (-688) (-706) (-5577) (-5600)

DN(∆) 031 023 022 025(796) (304) (203) (236)












65 Conclusion






0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

14

15

16

log(P(∆

))



BTCUSD

0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

log(P(∆

))



BCHUSD

0 1 2 3 4 5 6 7

log(∆)

9

10

11

12

13

14

15

log(P(∆

))



ETHUSD




0 1 2 3 4 5 6 7

log(∆)

4

6

8

10

12

14

16

18

20

log(P(∆

))



LTCUSD


1000

030

000

5000

070

000

0 100 200 300 400 500Delta


P(D

elta

)



95

Chapter 7



96

Chapter 8

Concluding Remarks

























102

Bibliography















Bibliography 103



















Bibliography 104

















Bibliography 105
















Bibliography 106

















Abstract

Acknowledgements

Synopsis

Motivation

Thesis Structure



Introduction





Variable Definition

Illiquidity

The Bid-Ask Spread


Data


Empirical Analysis





Conclusion



Introduction

















A Note on Causality

Conclusion


Introduction



Empirical Results

Conclusion


Concluding Remarks

Bibliography

iv



v


vi

Contents

Abstract iii

Acknowledgements v











vii





56 Conclusion 84




Bibliography 102

viii

List of Figures














ix

List of Tables







x



xi


xii


1

Chapter 1

Synopsis

11 Motivation















12 Thesis Structure



















analysts






8

Chapter 2



9

Chapter 3



31 Introduction



10



11







12




13






14




15




16



17





18


341 Illiquidity



VOLit(31)



19








(33)



20







21




SYNCHi = ln(

R2i

1minus R2i

)(34)


Rt = α + βRmt + εt (35)




22


Ψi = ln(

1minus R2i

R2i

)(36)


35 Data




23






24






25






26








27










28


Ψi = ln

(1minus R2

i

R2i

)(37)




i )R2i ) R2



j = 50

implies Ψj = 0





29







30











31





32








(310)



33





34


37 Conclusion



35



36






Total 248936 10000


37










t-statistic


t-statistic


t-statistic


t-statistic
















38








(3791) (3612) (1402) (805)


(1026) (minus103) (minus2388) (2127)



Control Variables


(128) (minus232) (329) (2498)


(823) (381) (437) (655)


(21319) (11015) (12855) (13415)



Observations 241598 80558 80277 80763R2 310 283 309 298



39











(minus1289) (099) (135) (minus820)

Control Variables


(6799) (3127) (852) (1792)




(864) (1333) (1132) (969)


(25037) (13739) (5984) (5036)

Observations 242170 80742 80578 80850R2 215 162 206 264



40






Total 1271 10000 389 10000

41

Chapter 4



42

Chapter 5


51 Introduction












-

-

-

-

-

-

-

-

-

-

-

MinimumTick Size

Demand(Bid)

Supply(Ask)

Bid-AskSpread

b(t)

a(t)

Price

















A Interest

A Principal

A Excess B Excess

B Principal
















































Panel A Sell Side

Received

Limit 9958 9976 9991 9975

Market 042 024 009 025

Done

Canceled 9836 9844 9881 9854

Filled 164 156 119 146

Panel B Buy Side

Received

Limit 9967 9979 9968 9971

Market 033 021 032 029

Done

Canceled 9922 9936 9877 9912

Filled 078 064 123 088


TAB

LE

54

Con

diti

onal

Prob

abili

ties

ofth

eO

rder

Type

inTi

me

tgiv

enth

eO

rder

Type

intminus

1

Not

eTh

eta

ble

show

sth

eco

ndit

iona

lpro

babi

litie

sof

spec

ific

orde

rty

pes

follo

win

gea

chot

her

inth

eor

der

book

Eac

hco

lum

nre

pres

ents

the

prob

abili

tyof

the

resp

ecti

veor

der

type

give

nth

eor

der

type

ofth

epr

evio

usor

der

equa

lsth

eor

der

type

ofth

ero

wT

heta

ble

can

bere

adas

follo

ws

The

cond

itio

nalp

roba

bilit

yth

ata

Smal

lBuy

Ord

eris

follo

wed

bya

Larg

eBu

yO

rder

is

Pro

b(L

arge

Buy t|S

mal

lBuy

tminus1)

=0

22

Th

esu

mof

each

row

equa

ls10

0

Not

eth

atth

esu

mof

each

colu

mn

does

not

give

the

unco

ndit

iona

lpro

babi

lity

ofth

ere

spec

tive

even

tSi

mila

rto

Biai

sH

illio

nan

dSp

att(

1995

)w

ede

fine

mar

keto

rder

sas

larg

eif

thei

rvo

lum

eex

ceed

sth

eex

isti

ngvo

lum

eat

the

best

bid

oras

kpr

ice

and

the

pric

eal

low

sth

eor

der

tocl

imb

thro

ugh

the

oppo

site

side

ofth

eor

der

book

Asm

allm

arke

tord

erdo

esno

tpos

sess

atle

asto

neof

thos

ech

arac

teri

stic

s

Tim

etminus

1Ti

me

tLa

rge

Buy

Smal

lBu

y

Buy

Ord

erin

side

Spre

ad

Buy

Ord

erat

Spre

ad

Buy

Ord

erbe

low

Spre

ad

Can

cele

dBu

yO

rder

Larg

eSe

llSm

all

Sell

Sell

Ord

erin

side

Spre

ad

Sell

Ord

erat

Spre

ad

Sell

Ord

erab

ove

Spre

ad

Can

cele

dSe

llO

rder

Larg

eBu

y1

13

258

12

86

213

14

43

354

50

06

010

2

13

045

15

11

135

8Sm

allB

uy0

22

258

4

09

224

21

76

332

40

05

029

0

72

169

12

60

205

2Bu

yO

rder

insi

deSp

read

005

0

11

676

5

68

174

550

58

001

0

04

235

0

47

720

9

29

Buy

Ord

erat

Spre

ad0

06

030

5

31

636

17

63

387

50

01

014

0

94

136

12

63

165

1Bu

yO

rder

belo

wSp

read

003

0

23

143

1

06

256

139

75

003

0

14

089

0

90

151

414

80

Can

cele

dBu

yO

rder

003

0

16

384

1

86

385

027

53

002

0

09

095

0

79

981

16

41

Larg

eSe

ll0

07

019

2

59

035

18

67

135

43

54

835

12

91

160

8

67

295

1Sm

allS

ell

004

0

48

070

2

10

182

824

51

073

5

53

827

2

20

119

425

23

Sell

Ord

erin

side

Spre

ad0

02

009

3

92

079

13

93

162

90

07

014

5

95

409

12

70

420

0Se

llO

rder

atSp

read

001

0

25

110

1

60

184

023

37

005

0

26

303

5

99

146

631

30

Sell

Ord

erab

ove

Spre

ad0

04

024

1

12

118

24

18

220

40

03

016

0

93

117

24

87

240

3C

ance

led

Sell

Ord

er0

02

015

1

27

104

13

67

256

80

02

011

2

98

181

26

72

265

4











Day 1Day 11


05

10152025


me (in BTC)

ΔDay 1

Day 11

Day 21

Day 31

Day 41

Day 51

05

10152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observa

tion Day

Aggrega

ted Ask

Volume

(in BTC

)

Δ







Day 1Day 11

Day 21Day 31

Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation D

ay

Bid Volume (

in BTC)

ΔDay 1

Day 11Day 21

Day 31Day 41

Day 51

0510152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observatio

n Day

Aggregated

Bid Volum

e (in BTC)

Δ







∆










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9262 8892 9885 9598 8653 9743

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9774 8263 9888 9700 8667 9786

p lt 010 p lt 005 p lt 001







∆




4

8

12

16

20

24

28

32


in BCH)


50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation Day

Aggregated Ask Vo

lume (in BCH)




2

4

6

8

10

12

14

16

18

20

22

24


e (in BCH)


0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observatio

n Day

Aggregated

Bid Volum

e (in BCH)










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 15178 15177 15178 101 100 101

R2 9574 8871 9992 9933 8198 9633

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 002 005


N 15178 15177 15178 101 100 101

R2 9822 8450 9947 9943 7864 9436

p lt 010 p lt 005 p lt 001




Day 1Day 6


020406080

100120140160180

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observati

on Day

Ask Volum

e (in ETH

)


05001000150020002500


ted Ask Volume

(in ETH)






020406080100120140


me (in ETH)


05001000150020002500



)Δ










∆







I∆ =

1 i f ∆ = 100 mod(0)0 else






Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 018 071


N 35395 35394 35395 101 100 101

R2 9861 8122 9844 9928 7496 9214

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 002




Std Err 046 0


N 35395 35394 35395 101 100 101

R2 9981 7718 9712 9849 7191 8995

p lt 010 p lt 005 p lt 001









I∆ 028 035 158 151 811 901

Std Err 0 0 004 003 008 136



N 50412 50412 15177 15177 35394 35394

R2 3105 600 1205 1606 2358 279

p lt 010 p lt 005 p lt 001























Ask Side



β-Coeff 064 086 104 017 074 118

Std Err 0 0 0 001 0 001


N 50412 15177 14 35394 100 100 100

R2 9985 9994 9994 8853 9982 9950


Bid Side



β-Coeff 065 087 108 013 059 109

Std Err 0 0 0 001 001 001


N 50412 15177 35394 100 100 100

R2 9971 9996 9999 7166 9926 9950


p lt 010 p lt 005 p lt 001






sumi=1

(1N

N

sumn=1

VOLinasktj

)(53)





SLOPEask =



(55)





2(56)




Volatilityit =








N o f Tradesit(59)














Model (1) (2) (3) (4) (5) (6)

SLOPE100 047 036 040

(265) (157) (148)

SLOPE10000 952 943 950

(172) (171) (166)

N of trades 004 004 005 005

(386) (361) (520) (410)

Trade size 073 071 071 073

(404) (382) (405) (390)

ln(MCAP) -287 273

(minus031) (035)

Spread -163 -319




R2 1120 2920 2920 960 2930 2940















Model (1) (2) (3) (4)

SLOPE100 1203 416

(873) (223)

SLOPE10000 6792 -6736

(143) (minus171)

Trade size -4395 -12390


ln(MCAP) 2416250 3267030

(403) (677)

Spread 453150 553640

(441) (545)



R2 5520 6340 3650 6290














Model (1) (2) (3) (4) (5) (6)

SLOPE100 037 037 044

(368) (298) (357)

SLOPE10000 560 597 561

(190) (181) (190)

Spread 071 084 161 136

(089) (098) (210) (162)




(minus043) (214)



R2 6080 6150 6070 5900 6030 5910










5 18 100 2077 000 2872 0005 18 10000 11883 000 2496 000


4 13 100 19617 000 1133 0024 13 10000 1315 001 4619 000


5 15 100 6630 000 1594 0015 15 10000 8031 000 1567 001







5 22 100 1027 007 4196 0002 51 10000 1232 000 662 004


5 14 100 2550 000 23472 0005 14 10000 2254 000 1765 000


1 68 100 854 000 001 0751 68 10000 249 012 023 063


56 Conclusion


85

Chapter 6



61 Introduction

















capitalization

















P(∆) =c

∆1+micro


= ln(c)︸︷︷︸β0






∆1+micro


= ln(c)︸︷︷︸β0



+ λ︸︷︷︸β2




Formula 61


DN(∆) =

1 i f ∆

100 isinN

0 else





micro -066 -065 -026 -025 -078 -077 085 085(-2670) (-2895) (-3142) (-3182) (-688) (-706) (-5577) (-5600)

DN(∆) 031 023 022 025(796) (304) (203) (236)












65 Conclusion






0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

14

15

16

log(P(∆

))



BTCUSD

0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

log(P(∆

))



BCHUSD

0 1 2 3 4 5 6 7

log(∆)

9

10

11

12

13

14

15

log(P(∆

))



ETHUSD




0 1 2 3 4 5 6 7

log(∆)

4

6

8

10

12

14

16

18

20

log(P(∆

))



LTCUSD


1000

030

000

5000

070

000

0 100 200 300 400 500Delta


P(D

elta

)



95

Chapter 7



96

Chapter 8

Concluding Remarks

























102

Bibliography















Bibliography 103



















Bibliography 104

















Bibliography 105
















Bibliography 106

















Abstract

Acknowledgements

Synopsis

Motivation

Thesis Structure



Introduction





Variable Definition

Illiquidity

The Bid-Ask Spread


Data


Empirical Analysis





Conclusion



Introduction

















A Note on Causality

Conclusion


Introduction



Empirical Results

Conclusion


Concluding Remarks

Bibliography

v


vi

Contents

Abstract iii

Acknowledgements v











vii





56 Conclusion 84




Bibliography 102

viii

List of Figures














ix

List of Tables







x



xi


xii


1

Chapter 1

Synopsis

11 Motivation















12 Thesis Structure



















analysts






8

Chapter 2



9

Chapter 3



31 Introduction



10



11







12




13






14




15




16



17





18


341 Illiquidity



VOLit(31)



19








(33)



20







21




SYNCHi = ln(

R2i

1minus R2i

)(34)


Rt = α + βRmt + εt (35)




22


Ψi = ln(

1minus R2i

R2i

)(36)


35 Data




23






24






25






26








27










28


Ψi = ln

(1minus R2

i

R2i

)(37)




i )R2i ) R2



j = 50

implies Ψj = 0





29







30











31





32








(310)



33





34


37 Conclusion



35



36






Total 248936 10000


37










t-statistic


t-statistic


t-statistic


t-statistic
















38








(3791) (3612) (1402) (805)


(1026) (minus103) (minus2388) (2127)



Control Variables


(128) (minus232) (329) (2498)


(823) (381) (437) (655)


(21319) (11015) (12855) (13415)



Observations 241598 80558 80277 80763R2 310 283 309 298



39











(minus1289) (099) (135) (minus820)

Control Variables


(6799) (3127) (852) (1792)




(864) (1333) (1132) (969)


(25037) (13739) (5984) (5036)

Observations 242170 80742 80578 80850R2 215 162 206 264



40






Total 1271 10000 389 10000

41

Chapter 4



42

Chapter 5


51 Introduction












-

-

-

-

-

-

-

-

-

-

-

MinimumTick Size

Demand(Bid)

Supply(Ask)

Bid-AskSpread

b(t)

a(t)

Price

















A Interest

A Principal

A Excess B Excess

B Principal
















































Panel A Sell Side

Received

Limit 9958 9976 9991 9975

Market 042 024 009 025

Done

Canceled 9836 9844 9881 9854

Filled 164 156 119 146

Panel B Buy Side

Received

Limit 9967 9979 9968 9971

Market 033 021 032 029

Done

Canceled 9922 9936 9877 9912

Filled 078 064 123 088


TAB

LE

54

Con

diti

onal

Prob

abili

ties

ofth

eO

rder

Type

inTi

me

tgiv

enth

eO

rder

Type

intminus

1

Not

eTh

eta

ble

show

sth

eco

ndit

iona

lpro

babi

litie

sof

spec

ific

orde

rty

pes

follo

win

gea

chot

her

inth

eor

der

book

Eac

hco

lum

nre

pres

ents

the

prob

abili

tyof

the

resp

ecti

veor

der

type

give

nth

eor

der

type

ofth

epr

evio

usor

der

equa

lsth

eor

der

type

ofth

ero

wT

heta

ble

can

bere

adas

follo

ws

The

cond

itio

nalp

roba

bilit

yth

ata

Smal

lBuy

Ord

eris

follo

wed

bya

Larg

eBu

yO

rder

is

Pro

b(L

arge

Buy t|S

mal

lBuy

tminus1)

=0

22

Th

esu

mof

each

row

equa

ls10

0

Not

eth

atth

esu

mof

each

colu

mn

does

not

give

the

unco

ndit

iona

lpro

babi

lity

ofth

ere

spec

tive

even

tSi

mila

rto

Biai

sH

illio

nan

dSp

att(

1995

)w

ede

fine

mar

keto

rder

sas

larg

eif

thei

rvo

lum

eex

ceed

sth

eex

isti

ngvo

lum

eat

the

best

bid

oras

kpr

ice

and

the

pric

eal

low

sth

eor

der

tocl

imb

thro

ugh

the

oppo

site

side

ofth

eor

der

book

Asm

allm

arke

tord

erdo

esno

tpos

sess

atle

asto

neof

thos

ech

arac

teri

stic

s

Tim

etminus

1Ti

me

tLa

rge

Buy

Smal

lBu

y

Buy

Ord

erin

side

Spre

ad

Buy

Ord

erat

Spre

ad

Buy

Ord

erbe

low

Spre

ad

Can

cele

dBu

yO

rder

Larg

eSe

llSm

all

Sell

Sell

Ord

erin

side

Spre

ad

Sell

Ord

erat

Spre

ad

Sell

Ord

erab

ove

Spre

ad

Can

cele

dSe

llO

rder

Larg

eBu

y1

13

258

12

86

213

14

43

354

50

06

010

2

13

045

15

11

135

8Sm

allB

uy0

22

258

4

09

224

21

76

332

40

05

029

0

72

169

12

60

205

2Bu

yO

rder

insi

deSp

read

005

0

11

676

5

68

174

550

58

001

0

04

235

0

47

720

9

29

Buy

Ord

erat

Spre

ad0

06

030

5

31

636

17

63

387

50

01

014

0

94

136

12

63

165

1Bu

yO

rder

belo

wSp

read

003

0

23

143

1

06

256

139

75

003

0

14

089

0

90

151

414

80

Can

cele

dBu

yO

rder

003

0

16

384

1

86

385

027

53

002

0

09

095

0

79

981

16

41

Larg

eSe

ll0

07

019

2

59

035

18

67

135

43

54

835

12

91

160

8

67

295

1Sm

allS

ell

004

0

48

070

2

10

182

824

51

073

5

53

827

2

20

119

425

23

Sell

Ord

erin

side

Spre

ad0

02

009

3

92

079

13

93

162

90

07

014

5

95

409

12

70

420

0Se

llO

rder

atSp

read

001

0

25

110

1

60

184

023

37

005

0

26

303

5

99

146

631

30

Sell

Ord

erab

ove

Spre

ad0

04

024

1

12

118

24

18

220

40

03

016

0

93

117

24

87

240

3C

ance

led

Sell

Ord

er0

02

015

1

27

104

13

67

256

80

02

011

2

98

181

26

72

265

4











Day 1Day 11


05

10152025


me (in BTC)

ΔDay 1

Day 11

Day 21

Day 31

Day 41

Day 51

05

10152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observa

tion Day

Aggrega

ted Ask

Volume

(in BTC

)

Δ







Day 1Day 11

Day 21Day 31

Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation D

ay

Bid Volume (

in BTC)

ΔDay 1

Day 11Day 21

Day 31Day 41

Day 51

0510152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observatio

n Day

Aggregated

Bid Volum

e (in BTC)

Δ







∆










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9262 8892 9885 9598 8653 9743

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9774 8263 9888 9700 8667 9786

p lt 010 p lt 005 p lt 001







∆




4

8

12

16

20

24

28

32


in BCH)


50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation Day

Aggregated Ask Vo

lume (in BCH)




2

4

6

8

10

12

14

16

18

20

22

24


e (in BCH)


0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observatio

n Day

Aggregated

Bid Volum

e (in BCH)










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 15178 15177 15178 101 100 101

R2 9574 8871 9992 9933 8198 9633

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 002 005


N 15178 15177 15178 101 100 101

R2 9822 8450 9947 9943 7864 9436

p lt 010 p lt 005 p lt 001




Day 1Day 6


020406080

100120140160180

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observati

on Day

Ask Volum

e (in ETH

)


05001000150020002500


ted Ask Volume

(in ETH)






020406080100120140


me (in ETH)


05001000150020002500



)Δ










∆







I∆ =

1 i f ∆ = 100 mod(0)0 else






Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 018 071


N 35395 35394 35395 101 100 101

R2 9861 8122 9844 9928 7496 9214

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 002




Std Err 046 0


N 35395 35394 35395 101 100 101

R2 9981 7718 9712 9849 7191 8995

p lt 010 p lt 005 p lt 001









I∆ 028 035 158 151 811 901

Std Err 0 0 004 003 008 136



N 50412 50412 15177 15177 35394 35394

R2 3105 600 1205 1606 2358 279

p lt 010 p lt 005 p lt 001























Ask Side



β-Coeff 064 086 104 017 074 118

Std Err 0 0 0 001 0 001


N 50412 15177 14 35394 100 100 100

R2 9985 9994 9994 8853 9982 9950


Bid Side



β-Coeff 065 087 108 013 059 109

Std Err 0 0 0 001 001 001


N 50412 15177 35394 100 100 100

R2 9971 9996 9999 7166 9926 9950


p lt 010 p lt 005 p lt 001






sumi=1

(1N

N

sumn=1

VOLinasktj

)(53)





SLOPEask =



(55)





2(56)




Volatilityit =








N o f Tradesit(59)














Model (1) (2) (3) (4) (5) (6)

SLOPE100 047 036 040

(265) (157) (148)

SLOPE10000 952 943 950

(172) (171) (166)

N of trades 004 004 005 005

(386) (361) (520) (410)

Trade size 073 071 071 073

(404) (382) (405) (390)

ln(MCAP) -287 273

(minus031) (035)

Spread -163 -319




R2 1120 2920 2920 960 2930 2940















Model (1) (2) (3) (4)

SLOPE100 1203 416

(873) (223)

SLOPE10000 6792 -6736

(143) (minus171)

Trade size -4395 -12390


ln(MCAP) 2416250 3267030

(403) (677)

Spread 453150 553640

(441) (545)



R2 5520 6340 3650 6290














Model (1) (2) (3) (4) (5) (6)

SLOPE100 037 037 044

(368) (298) (357)

SLOPE10000 560 597 561

(190) (181) (190)

Spread 071 084 161 136

(089) (098) (210) (162)




(minus043) (214)



R2 6080 6150 6070 5900 6030 5910










5 18 100 2077 000 2872 0005 18 10000 11883 000 2496 000


4 13 100 19617 000 1133 0024 13 10000 1315 001 4619 000


5 15 100 6630 000 1594 0015 15 10000 8031 000 1567 001







5 22 100 1027 007 4196 0002 51 10000 1232 000 662 004


5 14 100 2550 000 23472 0005 14 10000 2254 000 1765 000


1 68 100 854 000 001 0751 68 10000 249 012 023 063


56 Conclusion


85

Chapter 6



61 Introduction

















capitalization

















P(∆) =c

∆1+micro


= ln(c)︸︷︷︸β0






∆1+micro


= ln(c)︸︷︷︸β0



+ λ︸︷︷︸β2




Formula 61


DN(∆) =

1 i f ∆

100 isinN

0 else





micro -066 -065 -026 -025 -078 -077 085 085(-2670) (-2895) (-3142) (-3182) (-688) (-706) (-5577) (-5600)

DN(∆) 031 023 022 025(796) (304) (203) (236)












65 Conclusion






0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

14

15

16

log(P(∆

))



BTCUSD

0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

log(P(∆

))



BCHUSD

0 1 2 3 4 5 6 7

log(∆)

9

10

11

12

13

14

15

log(P(∆

))



ETHUSD




0 1 2 3 4 5 6 7

log(∆)

4

6

8

10

12

14

16

18

20

log(P(∆

))



LTCUSD


1000

030

000

5000

070

000

0 100 200 300 400 500Delta


P(D

elta

)



95

Chapter 7



96

Chapter 8

Concluding Remarks

























102

Bibliography















Bibliography 103



















Bibliography 104

















Bibliography 105
















Bibliography 106

















Abstract

Acknowledgements

Synopsis

Motivation

Thesis Structure



Introduction





Variable Definition

Illiquidity

The Bid-Ask Spread


Data


Empirical Analysis





Conclusion



Introduction

















A Note on Causality

Conclusion


Introduction



Empirical Results

Conclusion


Concluding Remarks

Bibliography

vi

Contents

Abstract iii

Acknowledgements v











vii





56 Conclusion 84




Bibliography 102

viii

List of Figures














ix

List of Tables







x



xi


xii


1

Chapter 1

Synopsis

11 Motivation















12 Thesis Structure



















analysts






8

Chapter 2



9

Chapter 3



31 Introduction



10



11







12




13






14




15




16



17





18


341 Illiquidity



VOLit(31)



19








(33)



20







21




SYNCHi = ln(

R2i

1minus R2i

)(34)


Rt = α + βRmt + εt (35)




22


Ψi = ln(

1minus R2i

R2i

)(36)


35 Data




23






24






25






26








27










28


Ψi = ln

(1minus R2

i

R2i

)(37)




i )R2i ) R2



j = 50

implies Ψj = 0





29







30











31





32








(310)



33





34


37 Conclusion



35



36






Total 248936 10000


37










t-statistic


t-statistic


t-statistic


t-statistic
















38








(3791) (3612) (1402) (805)


(1026) (minus103) (minus2388) (2127)



Control Variables


(128) (minus232) (329) (2498)


(823) (381) (437) (655)


(21319) (11015) (12855) (13415)



Observations 241598 80558 80277 80763R2 310 283 309 298



39











(minus1289) (099) (135) (minus820)

Control Variables


(6799) (3127) (852) (1792)




(864) (1333) (1132) (969)


(25037) (13739) (5984) (5036)

Observations 242170 80742 80578 80850R2 215 162 206 264



40






Total 1271 10000 389 10000

41

Chapter 4



42

Chapter 5


51 Introduction












-

-

-

-

-

-

-

-

-

-

-

MinimumTick Size

Demand(Bid)

Supply(Ask)

Bid-AskSpread

b(t)

a(t)

Price

















A Interest

A Principal

A Excess B Excess

B Principal
















































Panel A Sell Side

Received

Limit 9958 9976 9991 9975

Market 042 024 009 025

Done

Canceled 9836 9844 9881 9854

Filled 164 156 119 146

Panel B Buy Side

Received

Limit 9967 9979 9968 9971

Market 033 021 032 029

Done

Canceled 9922 9936 9877 9912

Filled 078 064 123 088


TAB

LE

54

Con

diti

onal

Prob

abili

ties

ofth

eO

rder

Type

inTi

me

tgiv

enth

eO

rder

Type

intminus

1

Not

eTh

eta

ble

show

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eco

ndit

iona

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sof

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ific

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her

inth

eor

der

book

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nre

pres

ents

the

prob

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tyof

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resp

ecti

veor

der

type

give

nth

eor

der

type

ofth

epr

evio

usor

der

equa

lsth

eor

der

type

ofth

ero

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heta

ble

can

bere

adas

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ws

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cond

itio

nalp

roba

bilit

yth

ata

Smal

lBuy

Ord

eris

follo

wed

bya

Larg

eBu

yO

rder

is

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b(L

arge

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mal

lBuy

tminus1)

=0

22

Th

esu

mof

each

row

equa

ls10

0

Not

eth

atth

esu

mof

each

colu

mn

does

not

give

the

unco

ndit

iona

lpro

babi

lity

ofth

ere

spec

tive

even

tSi

mila

rto

Biai

sH

illio

nan

dSp

att(

1995

)w

ede

fine

mar

keto

rder

sas

larg

eif

thei

rvo

lum

eex

ceed

sth

eex

isti

ngvo

lum

eat

the

best

bid

oras

kpr

ice

and

the

pric

eal

low

sth

eor

der

tocl

imb

thro

ugh

the

oppo

site

side

ofth

eor

der

book

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atle

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ech

arac

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stic

s

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me

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rge

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lBu

y

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erin

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erbe

low

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ad

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cele

dBu

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llSm

all

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erat

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ad

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Ord

erab

ove

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ad

Can

cele

dSe

llO

rder

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eBu

y1

13

258

12

86

213

14

43

354

50

06

010

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13

045

15

11

135

8Sm

allB

uy0

22

258

4

09

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0

72

169

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60

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005

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1

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104

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256

80

02

011

2

98

181

26

72

265

4











Day 1Day 11


05

10152025


me (in BTC)

ΔDay 1

Day 11

Day 21

Day 31

Day 41

Day 51

05

10152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observa

tion Day

Aggrega

ted Ask

Volume

(in BTC

)

Δ







Day 1Day 11

Day 21Day 31

Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation D

ay

Bid Volume (

in BTC)

ΔDay 1

Day 11Day 21

Day 31Day 41

Day 51

0510152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observatio

n Day

Aggregated

Bid Volum

e (in BTC)

Δ







∆










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9262 8892 9885 9598 8653 9743

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9774 8263 9888 9700 8667 9786

p lt 010 p lt 005 p lt 001







∆




4

8

12

16

20

24

28

32


in BCH)


50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation Day

Aggregated Ask Vo

lume (in BCH)




2

4

6

8

10

12

14

16

18

20

22

24


e (in BCH)


0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observatio

n Day

Aggregated

Bid Volum

e (in BCH)










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 15178 15177 15178 101 100 101

R2 9574 8871 9992 9933 8198 9633

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 002 005


N 15178 15177 15178 101 100 101

R2 9822 8450 9947 9943 7864 9436

p lt 010 p lt 005 p lt 001




Day 1Day 6


020406080

100120140160180

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observati

on Day

Ask Volum

e (in ETH

)


05001000150020002500


ted Ask Volume

(in ETH)






020406080100120140


me (in ETH)


05001000150020002500



)Δ










∆







I∆ =

1 i f ∆ = 100 mod(0)0 else






Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 018 071


N 35395 35394 35395 101 100 101

R2 9861 8122 9844 9928 7496 9214

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 002




Std Err 046 0


N 35395 35394 35395 101 100 101

R2 9981 7718 9712 9849 7191 8995

p lt 010 p lt 005 p lt 001









I∆ 028 035 158 151 811 901

Std Err 0 0 004 003 008 136



N 50412 50412 15177 15177 35394 35394

R2 3105 600 1205 1606 2358 279

p lt 010 p lt 005 p lt 001























Ask Side



β-Coeff 064 086 104 017 074 118

Std Err 0 0 0 001 0 001


N 50412 15177 14 35394 100 100 100

R2 9985 9994 9994 8853 9982 9950


Bid Side



β-Coeff 065 087 108 013 059 109

Std Err 0 0 0 001 001 001


N 50412 15177 35394 100 100 100

R2 9971 9996 9999 7166 9926 9950


p lt 010 p lt 005 p lt 001






sumi=1

(1N

N

sumn=1

VOLinasktj

)(53)





SLOPEask =



(55)





2(56)




Volatilityit =








N o f Tradesit(59)














Model (1) (2) (3) (4) (5) (6)

SLOPE100 047 036 040

(265) (157) (148)

SLOPE10000 952 943 950

(172) (171) (166)

N of trades 004 004 005 005

(386) (361) (520) (410)

Trade size 073 071 071 073

(404) (382) (405) (390)

ln(MCAP) -287 273

(minus031) (035)

Spread -163 -319




R2 1120 2920 2920 960 2930 2940















Model (1) (2) (3) (4)

SLOPE100 1203 416

(873) (223)

SLOPE10000 6792 -6736

(143) (minus171)

Trade size -4395 -12390


ln(MCAP) 2416250 3267030

(403) (677)

Spread 453150 553640

(441) (545)



R2 5520 6340 3650 6290














Model (1) (2) (3) (4) (5) (6)

SLOPE100 037 037 044

(368) (298) (357)

SLOPE10000 560 597 561

(190) (181) (190)

Spread 071 084 161 136

(089) (098) (210) (162)




(minus043) (214)



R2 6080 6150 6070 5900 6030 5910










5 18 100 2077 000 2872 0005 18 10000 11883 000 2496 000


4 13 100 19617 000 1133 0024 13 10000 1315 001 4619 000


5 15 100 6630 000 1594 0015 15 10000 8031 000 1567 001







5 22 100 1027 007 4196 0002 51 10000 1232 000 662 004


5 14 100 2550 000 23472 0005 14 10000 2254 000 1765 000


1 68 100 854 000 001 0751 68 10000 249 012 023 063


56 Conclusion


85

Chapter 6



61 Introduction

















capitalization

















P(∆) =c

∆1+micro


= ln(c)︸︷︷︸β0






∆1+micro


= ln(c)︸︷︷︸β0



+ λ︸︷︷︸β2




Formula 61


DN(∆) =

1 i f ∆

100 isinN

0 else





micro -066 -065 -026 -025 -078 -077 085 085(-2670) (-2895) (-3142) (-3182) (-688) (-706) (-5577) (-5600)

DN(∆) 031 023 022 025(796) (304) (203) (236)












65 Conclusion






0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

14

15

16

log(P(∆

))



BTCUSD

0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

log(P(∆

))



BCHUSD

0 1 2 3 4 5 6 7

log(∆)

9

10

11

12

13

14

15

log(P(∆

))



ETHUSD




0 1 2 3 4 5 6 7

log(∆)

4

6

8

10

12

14

16

18

20

log(P(∆

))



LTCUSD


1000

030

000

5000

070

000

0 100 200 300 400 500Delta


P(D

elta

)



95

Chapter 7



96

Chapter 8

Concluding Remarks

























102

Bibliography















Bibliography 103



















Bibliography 104

















Bibliography 105
















Bibliography 106

















Abstract

Acknowledgements

Synopsis

Motivation

Thesis Structure



Introduction





Variable Definition

Illiquidity

The Bid-Ask Spread


Data


Empirical Analysis





Conclusion



Introduction

















A Note on Causality

Conclusion


Introduction



Empirical Results

Conclusion


Concluding Remarks

Bibliography

vii





56 Conclusion 84




Bibliography 102

viii

List of Figures














ix

List of Tables







x



xi


xii


1

Chapter 1

Synopsis

11 Motivation















12 Thesis Structure



















analysts






8

Chapter 2



9

Chapter 3



31 Introduction



10



11







12




13






14




15




16



17





18


341 Illiquidity



VOLit(31)



19








(33)



20







21




SYNCHi = ln(

R2i

1minus R2i

)(34)


Rt = α + βRmt + εt (35)




22


Ψi = ln(

1minus R2i

R2i

)(36)


35 Data




23






24






25






26








27










28


Ψi = ln

(1minus R2

i

R2i

)(37)




i )R2i ) R2



j = 50

implies Ψj = 0





29







30











31





32








(310)



33





34


37 Conclusion



35



36






Total 248936 10000


37










t-statistic


t-statistic


t-statistic


t-statistic
















38








(3791) (3612) (1402) (805)


(1026) (minus103) (minus2388) (2127)



Control Variables


(128) (minus232) (329) (2498)


(823) (381) (437) (655)


(21319) (11015) (12855) (13415)



Observations 241598 80558 80277 80763R2 310 283 309 298



39











(minus1289) (099) (135) (minus820)

Control Variables


(6799) (3127) (852) (1792)




(864) (1333) (1132) (969)


(25037) (13739) (5984) (5036)

Observations 242170 80742 80578 80850R2 215 162 206 264



40






Total 1271 10000 389 10000

41

Chapter 4



42

Chapter 5


51 Introduction












-

-

-

-

-

-

-

-

-

-

-

MinimumTick Size

Demand(Bid)

Supply(Ask)

Bid-AskSpread

b(t)

a(t)

Price

















A Interest

A Principal

A Excess B Excess

B Principal
















































Panel A Sell Side

Received

Limit 9958 9976 9991 9975

Market 042 024 009 025

Done

Canceled 9836 9844 9881 9854

Filled 164 156 119 146

Panel B Buy Side

Received

Limit 9967 9979 9968 9971

Market 033 021 032 029

Done

Canceled 9922 9936 9877 9912

Filled 078 064 123 088


TAB

LE

54

Con

diti

onal

Prob

abili

ties

ofth

eO

rder

Type

inTi

me

tgiv

enth

eO

rder

Type

intminus

1

Not

eTh

eta

ble

show

sth

eco

ndit

iona

lpro

babi

litie

sof

spec

ific

orde

rty

pes

follo

win

gea

chot

her

inth

eor

der

book

Eac

hco

lum

nre

pres

ents

the

prob

abili

tyof

the

resp

ecti

veor

der

type

give

nth

eor

der

type

ofth

epr

evio

usor

der

equa

lsth

eor

der

type

ofth

ero

wT

heta

ble

can

bere

adas

follo

ws

The

cond

itio

nalp

roba

bilit

yth

ata

Smal

lBuy

Ord

eris

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4











Day 1Day 11


05

10152025


me (in BTC)

ΔDay 1

Day 11

Day 21

Day 31

Day 41

Day 51

05

10152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observa

tion Day

Aggrega

ted Ask

Volume

(in BTC

)

Δ







Day 1Day 11

Day 21Day 31

Day 41Day 51

05

10152025

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation D

ay

Bid Volume (

in BTC)

ΔDay 1

Day 11Day 21

Day 31Day 41

Day 51

0510152025303540

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340

Observatio

n Day

Aggregated

Bid Volum

e (in BTC)

Δ







∆










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9262 8892 9885 9598 8653 9743

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 0 0


N 50413 50412 50413 101 100 101

R2 9774 8263 9888 9700 8667 9786

p lt 010 p lt 005 p lt 001







∆




4

8

12

16

20

24

28

32


in BCH)


50

100

150

200

250

300

350

400

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observation Day

Aggregated Ask Vo

lume (in BCH)




2

4

6

8

10

12

14

16

18

20

22

24


e (in BCH)


0

50

100

150

200

250

300

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observatio

n Day

Aggregated

Bid Volum

e (in BCH)










Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 0 0


N 15178 15177 15178 101 100 101

R2 9574 8871 9992 9933 8198 9633

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 0




Std Err 002 005


N 15178 15177 15178 101 100 101

R2 9822 8450 9947 9943 7864 9436

p lt 010 p lt 005 p lt 001




Day 1Day 6


020406080

100120140160180

0 20 40 60 80 100 120 140 160 180 200 220 240 260 280 300 320 340 Observati

on Day

Ask Volum

e (in ETH

)


05001000150020002500


ted Ask Volume

(in ETH)






020406080100120140


me (in ETH)


05001000150020002500



)Δ










∆







I∆ =

1 i f ∆ = 100 mod(0)0 else






Ask Side


Model (11) (21) (31) (12) (22) (32)


Std Err 0 0




Std Err 018 071


N 35395 35394 35395 101 100 101

R2 9861 8122 9844 9928 7496 9214

Bid Side


Model (13) (23) (33) (14) (24) (34)


Std Err 0 002




Std Err 046 0


N 35395 35394 35395 101 100 101

R2 9981 7718 9712 9849 7191 8995

p lt 010 p lt 005 p lt 001









I∆ 028 035 158 151 811 901

Std Err 0 0 004 003 008 136



N 50412 50412 15177 15177 35394 35394

R2 3105 600 1205 1606 2358 279

p lt 010 p lt 005 p lt 001























Ask Side



β-Coeff 064 086 104 017 074 118

Std Err 0 0 0 001 0 001


N 50412 15177 14 35394 100 100 100

R2 9985 9994 9994 8853 9982 9950


Bid Side



β-Coeff 065 087 108 013 059 109

Std Err 0 0 0 001 001 001


N 50412 15177 35394 100 100 100

R2 9971 9996 9999 7166 9926 9950


p lt 010 p lt 005 p lt 001






sumi=1

(1N

N

sumn=1

VOLinasktj

)(53)





SLOPEask =



(55)





2(56)




Volatilityit =








N o f Tradesit(59)














Model (1) (2) (3) (4) (5) (6)

SLOPE100 047 036 040

(265) (157) (148)

SLOPE10000 952 943 950

(172) (171) (166)

N of trades 004 004 005 005

(386) (361) (520) (410)

Trade size 073 071 071 073

(404) (382) (405) (390)

ln(MCAP) -287 273

(minus031) (035)

Spread -163 -319




R2 1120 2920 2920 960 2930 2940















Model (1) (2) (3) (4)

SLOPE100 1203 416

(873) (223)

SLOPE10000 6792 -6736

(143) (minus171)

Trade size -4395 -12390


ln(MCAP) 2416250 3267030

(403) (677)

Spread 453150 553640

(441) (545)



R2 5520 6340 3650 6290














Model (1) (2) (3) (4) (5) (6)

SLOPE100 037 037 044

(368) (298) (357)

SLOPE10000 560 597 561

(190) (181) (190)

Spread 071 084 161 136

(089) (098) (210) (162)




(minus043) (214)



R2 6080 6150 6070 5900 6030 5910










5 18 100 2077 000 2872 0005 18 10000 11883 000 2496 000


4 13 100 19617 000 1133 0024 13 10000 1315 001 4619 000


5 15 100 6630 000 1594 0015 15 10000 8031 000 1567 001







5 22 100 1027 007 4196 0002 51 10000 1232 000 662 004


5 14 100 2550 000 23472 0005 14 10000 2254 000 1765 000


1 68 100 854 000 001 0751 68 10000 249 012 023 063


56 Conclusion


85

Chapter 6



61 Introduction

















capitalization

















P(∆) =c

∆1+micro


= ln(c)︸︷︷︸β0






∆1+micro


= ln(c)︸︷︷︸β0



+ λ︸︷︷︸β2




Formula 61


DN(∆) =

1 i f ∆

100 isinN

0 else





micro -066 -065 -026 -025 -078 -077 085 085(-2670) (-2895) (-3142) (-3182) (-688) (-706) (-5577) (-5600)

DN(∆) 031 023 022 025(796) (304) (203) (236)












65 Conclusion






0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

14

15

16

log(P(∆

))



BTCUSD

0 1 2 3 4 5 6 7

log(∆)

8

9

10

11

12

13

log(P(∆

))



BCHUSD

0 1 2 3 4 5 6 7

log(∆)

9

10

11

12

13

14

15

log(P(∆

))



ETHUSD




0 1 2 3 4 5 6 7

log(∆)

4

6

8

10

12

14

16

18

20

log(P(∆

))



LTCUSD


1000

030

000

5000

070

000

0 100 200 300 400 500Delta


P(D

elta

)



95

Chapter 7



96

Chapter 8

Concluding Remarks

























102

Bibliography















Bibliography 103



















Bibliography 104

















Bibliography 105
















Bibliography 106

















Abstract

Acknowledgements

Synopsis

Motivation

Thesis Structure



Introduction





Variable Definition

Illiquidity

The Bid-Ask Spread


Data


Empirical Analysis





Conclusion



Introduction

















A Note on Causality

Conclusion


Introduction



Empirical Results

Conclusion


Concluding Remarks

Bibliography

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