VYTAUTAS MAGNUS UNIVERSITY

FACULTY OF ECONOMICS AND MANAGEMENT

Vytautas Strimaitis

LONDON STOCK EXCHANGE AND INDIVIDUAL COMPANIES REACTION

TO “BREXIT” NEWS

Final Master Thesis

Finance study programme, state code 6211LX042

Finance study field

Supervisor: Assoc. prof. dr. Renata Legenzova (degree, academic position, name and last name)

Defended: Dean assoc. prof., dr. Rita Bendaravičienė

Kaunas, 2020

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SANTRAUKA

Baigiamojo darbo autorius: (Vytautas Strimaitis)

Pilnas baigiamojo darbo

pavadinimas:

(Jungtinės Karalystės Finansų Rinkos ir Individualių

Kompanijų Reakcija į „BREXIT“ Naujienas)

Baigiamojo darbo vadovas: (Assoc. prof. dr. Renata Legenzova)

Baigiamojo darbo atlikimo

vieta ir metai:

Vytauto Didžiojo universitetas, Ekonomikos ir vadybos

fakultetas, Kaunas, 2020

Puslapių skaičius: (65)

Lentelių skaičius: (l2)

Paveikslų skaičius: (8)

Priedų skaičius: (8)

Raktiniai žodžiai (International financial markets (G15), Event studies (G14),

Portfolio Choice (G11) )

Tyrimo metu tiriama, kaip “BREXIT” naujienos veikia Jungtinės Karalystės finansų rinką ir

atskiras kompanijas. Darbas apžvelgia kokie veiksniai veikia finansų rinkas ir investuotojų elgseną.

Darbe išskiriamas ir didesnis dėmesys sutelkiamas į politinį nestabilumą ir kaip jis veikia rinkas ir

investuotojus. Apžvelgiami egzistuojantys matematiniai modeliai, kuriais matuojamas politinio

nestabilumo poveikis finansų rinkoms. Darbe buvo pritaikytas “Event-study” metodas nustatant

kaip “BREXIT” naujienos paveikė Jungtinės Karalystės finansų rinkas ir individualias kompanijas,

kuriomis prekiaujams Jungtinės Karalystės akcijų biržoje. Rezultatai parodė, jog iš 17 atrinktų

“BREXIT” įvykių įtakos Jungtinės Karalystės finansų rinkai turėjo tik 5. Individualioms

kompanijoms naujienos susijusios su “BREXIT” turėjo reikšmingą poveikį. Didžiajai daliai

kompanijų įtrauktų į imtį “BREXIT” naujienos turėjo negiamą poveikį dvejuose laiko perioduose: 2

dienos iki ir po įvykio bei 10 dienų iki įvykio. “BREXIT” naujienų poveikis finansų sektoriaus

kompanijoms buvo stipresnis nei kitų sektorių kompanijoms.

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ABSTRACT

Author of Final Master

Thesis:

(Vytautas Strimaitis)

Full title of Final Master

Thesis:

(London Stock Exchange and Individual Companies

Reaction to “BREXIT” News)

Supervisor: Assoc. prof. dr. Renata Legenzova

Presented at: Vytautas Magnus University, Faculty of Economics and

Management, Kaunas, 2020

Number of pages: (65)

Number of tables: (12)

Number of pictures: (8)

Number of annexes: (8)

Key words (International financial markets (G15), Event studies (G14),

Portfolio Choice (G11))

The main problem of the research is how “BREXIT” news affect the UK financial market

and individual companies. Research analyses factors affecting financial markets, individual

companies and investor behaviour. Research concentrates on how political instability affects

financial markets, individual companies and investor behaviour. Furthermore the empirical models

evaluating the effect of political instability are analysed in the work. Research applies “Event-

study” method and estimates how “BREXIT” news affect UK financial market and individual

companies listed on UK financial market. Results determined that “BREXIT” news were

insignificant to the UK financial market because only 5 out 17 events were significant to returns of

it. However analyses of individual companies determined that “BREXIT” news had a significant

effect to them. Larger number of companies of the sample experienced significant negative changes

in their returns due to the “BREXIT” events. Two “event-windows” proved it. “BREXIT” news

effect on financial sector companies was stronger compared to other to other sector companies.

CONTENT

Introduction ............................................................................................................................... 5

I. Theoretical Aspects Of Finance Theories, Models And Political Stability And Their

Influence On Financial Markets .............................................................................................. 7

1.1 Comparative Analysis of Traditional and Behavioral Finance Theories ........................... 7

1.2 Traditional and Behavioral Factors Affecting Stock Markets and Prices .......................... 11

1.3 Financial Players’ Behavior Influenced by Political Uncertainty ...................................... 15

1.4 Empirical Models for Events Effect Estimation ................................................................ 17

1.5 Overview of Previous Research on “BREXIT” Effect to Financial Market ...................... 19

II. “BREXIT” News Effect For “London Stock Exchange” And Individual Companies

Methodological Justification ..................................................................................................... 25

2.1 Relevance and Aim of the Research .................................................................................. 25

2.2 Research Hypothesis .......................................................................................................... 27

2.3 Logic and Argumentation of Research Model ................................................................... 30

2.4 Data Sample of the Research ............................................................................................. 34

2.5 Limitations of the Empirical Model ................................................................................... 36

III. Empirical Results On “BREXIT” Effect For London Stock Exchange, Individual

Companies And Various Sectors .............................................................................................. 39

3.1 Descriptive Statistics of London Stock Exchange, Individual Companies and Various UK

sectors ................................................................................................................................. 39

3.2 “BREXIT” News Effect for London Stock Exchange ....................................................... 42

3.3 “BREXIT” News Effect for Individual Companies ........................................................... 45

3.4 “BREXIT” News Effect for Various United Kingdom Economical Sectors ..................... 51

3.5 Discussion of Research Results ......................................................................................... 53

3.6 Limitations of the Empirical Data Findings and Reccomendations for Future Researches .... 57

Conclusions ................................................................................................................................ 59

References................................................................................................................................... 61

Annexes ....................................................................................................................................... 66

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INTRODUCTION

Relevance and topicality. Financial markets are sensitive to factors that occur in the world

and markets and their occurrence have an effect for their performance. Nevertheless, huge role for

price indexes of financial markets has a politics and political news, announcements around the

world and the country the markets trade. One of the major political news that hit the EU and world

was UK’s withdrawal from the European Union announced in 2016 by British referendum. It

affected not only UK financial markets but other countries and members of EU as well. This

agreement to leave European Union got the name of “BREXIT”. For the past years British

referendum announced a lot of news towards “BREXIT” and with further announcement of them

markets reacted differently. Actual day of the agreement to leave EU was delayed and etc. creating

a lot of chaos for the past few years not only in the UK financial markets but also worldwide So it is

important to estimate how strongly “BREXIT” news affects the UK financial market and individual

companies that are listed in UK’s financial market. Previous researches on this topic determined

that “BREXIT” affected negatively United Kingdom financial market (Guedes, Ferreira, Dionisio

and Zebende, 2019). Various economical sectors of United Kingdom also suffered due to the

“BREXIT” (Ramiah, Pham, Moosa, 2017). According to their results financial sector companies

experienced -15.37% loses on 10 days period. Insurance sector companies experienced loss of -

8.18%. Lastly travel and leisure sector companies experienced loses of -3.64% on 10 day period.

Lastly close trade partners of UK also suffered the consequences of “BREXIT” referendum

(Abraham, 2018). NZX 50 index returns decreased by more than 4% on 20 days period until the

referendum vote.

Research object in this research are “London Stock Exchange” and largest individual

companies that are listed on “London Stock Exchange”.

Research problem addressed in this paper is what UK stock market reaction to

“BREXIT” news announcements.

The aim of this research is to analyze what effect has news about “BREXIT” to UK stock

market and individual companies. To reach the aim of the paper the following objectives were set:

1. Define the meaning of traditional and behavior finance and analyze the estimation

models of traditional and behavior finance

2. Name factors affecting stock prices movements

3. Define political uncertainty and its effect on financial markets.

4. To analyze the existing models and results of previous researches of “BREXIT”

effect for financial markets and individual companies

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5. Present the results of the research analyzing how „BREXIT“ news affects UK

financial market and individual companies.

To determine named objectives theoretical and practical analyzes will be applied. For

theoretical analyzes part previous authors research papers will be analyzed and compared. Based on

their results the research aim and problem will be explained. Furthermore the analyzes of previous

researches will contemplate naming research hypothesis and empirical model. Lastly empirical

model based on other authors work will be applied in order to approve or reject the hypothesis of

the research.

Scientific literature for the research were selected from the „Science Direct“, „EBSCO“

scientificic literature websites. Empirical data for the research were selected from „investing.com“

and „londonstockexchange.com“ online websites.

The paper is structured in 3 main parts:

The first part of paper analyses theoretical aspects of the work. In the first part traditional

and behavioural finance theories definitions, models, factors affecting stock prices movements are

analysed. Furthermore analyses of political instability effect to financial markets are presented.

Empirical models estimating the effect of such events to financial markets and previous works on

how “BREXIT” effected financial markets are presented.

The second part of paper develops the method of the research. In the second part of the

work hypothesis based on previous authors works are constructed. Furthermore the relevance and

necessity of the research is reasoned. Lastly the empirical model and data for the research are

presented.

In the third part results of the empirical model are presented. It shows how “BREXIT”

events affected UK financial market, individual companies with largest market capitalization listed

on UK financial market and various economy sectors of UK. Furthermore the hypothesis results are

presented. Lastly limitations of the empirical research and recommendations for future researches

are presented.

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I. THEORETICAL ASPECTS OF FINANCE THEORIES, MODELS

AND POLITICAL STABILITY AND THEIR INFLUENCE ON

FINANCIAL MARKETS

In the first chapter of research theoretical aspects of the work will be analyzed. In the first

part traditional and behavioural finance theories definitions, models, factors affecting stock prices

movements are analysed. Furthermore analyses of political instability effect to financial markets are

presented. Empirical models estimating the effect of such events to financial markets and previous

works on how “BREXIT” effected financial markets are presented.

1.1 Comparative Analysis of Traditional and Behavioral Finance

Theories

In this section the financial theories and their models existing in the financial world will be

analyzed, described and compared. In general there exist two different theories in financial sciences

on stock market prices movements. The earlier one are called “Traditional finance theory” which

started in mid-eighteenth century when John Stuart Mill in 1844 years introduced the concept of the

“Economic man” also called “homo economicus”(Kapoor and Prosad, 2017). Traditional theory

focuses on the concept of “expected utility theory”. Utility in traditional finance theory is

considered as a measure of satisfaction of individuals consuming by good of a service. “Homo

economicus” is the person who tries to maximize his satisfaction with his limited resources and

possibilities. Yoshinaga and Ramalho (2014) supplement the concept of traditional finance theory

and definition of “homo economicus” by adding that economic subjects like “homo economicus”

makes decisions with unlimited rationality, present risk aversion and aim to maximize “expected

utility” at every decision he makes. After John Stuart Mill (1844) introduction of rational investor

concept whose main object is to maximize his utility others authors followed and improved this

theory. Two major theory models were “Markowitz portfolio theory” by Harry Markowitz in 1952

and “Efficient Market Hypothesis” by Eugene Fama in 1970 (Kapoor and Prosad, 2017).

“Markowitz portfolio theory” created the capital asset pricing model which was the most central

assets pricing model in finance until “Efficient Market Hypothesis” creation, after it “Markowitz

portfolio theory” was pushed away because Markowitz portfolio theory could not explain anomalies

in its theory results. Matsumura and Kawamoto (2013) supplement the traditional finance theory

with the explanation for “Efficient market hypothesis”. Authors define it as the fact that stock prices

are affected by the profits of the future profits of companies and information is used efficiently in

anticipation formation. Matsumura and Kawamoto (2013) explain that in efficient markets

information about company is immediately analyzed by investors and reflect the rational decisions

of the investor. Traditional finance theory is based on rational financial player whose purpose is to

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maximize his utility and all information available in traditional finance theory is constructed to fit

the calculations. Traditional theories couldn’t explain the anomalies and disruptions in stock

markets. It couldn’t explain the stock market bubbles, under-reaction, investor sentiment and etc.

That’s when the new field of financial theory started to evolve and it was named “behavioral

finance”. Main purpose of it was to investigate those anomalies that traditional finance couldn’t

explain (Kapoor and Prosad, 2017). The backbone of behavioral finance theory was formatted by

the psychologists when they introduced the concept of “prospect theory” for analysis of decision

making under risk (Kapoor and Prosad, 2017). “Prospect theory” critique the expected utility theory

(traditional finance) as a tool for decision-making in situations involving uncertainty and risk,

adopting as premises the presence of irrationality and the corresponding use of heuristics in

people’s decision-making processes, leading to systematic errors due to biased cognitive processes

(Yoshinaga and Ramalho, 2014). “Prospect theory” explains how people overestimate the changes

involving losses and underestimate the changes involving gains. Even small loses has a higher value

than the same gains, meaning that loss in 2 would be higher than gain in 2. Filbeck et al., (2017) in

their work explain behavioral finance as the field of finance that proposes psychology based

theories to explain stock market anomalies such as severe rises or falls in stock price. They do not

provide such a deep analysis to behavioral finance theory as authors mentioned before but correctly,

strictly and similar to others interpreter the definition and purpose of behavioral finance. Moreover,

Divanoglu and Bagci (2018) defined behavioral finance similarly to other authors but also

implementing that decisions of investors are also affected by sociological factors. They state that

when it comes to investing individual investor can’t act rationally but there exists continues

rationality in traditional financial understanding (Divanoglu and Bagci, 2018). Authors specifically

name what psychological concepts effect the investor decisions such as biases, overconfidence and

etc. Ramiah, Xu and Moosa (2015) analyzes the definition of behavioral finance and they also

describe characteristics of behavioral finance investor. They consider investor not as “rational” as

traditional theory scientists but as how they describe it “normal”. It means that investor can make

mistakes in his valuations (cognitive errors) (Ramiah, Xu and Moosa, 2015).

After analyzing the concept of traditional and behavioral finance theory models of those

two theories will be analyzed. Traditional finance theory as it was mentioned before is based on the

two concepts, risk and return. Financial player always tries to maximize his utility and he is aware

of his risks of loss and always stays rational. In traditional finance theory all the financial decisions

are based on the strict mathematical calculations. Sushma and Rushdi (2018) in his work perfectly

describe and name all existing mathematical theory models for estimation of stock prices. He names

five most important theories in traditional finance: Expected utility theory, Modern portfolio theory,

Capital pricing model, efficient market hypothesis, Arbitrage pricing theory.

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Expected utility theory is based on the decision-making under risk. The utility of a risky

prospect is given by the sum of the utilities u of the alternative possible outcomes of the prospect,

each weighted by the probability that the outcome will occur (Moscati, 2016). Furthermore author

gives an example with lottery ticket and trip to London where the decision maker according to

expected utility theory shall choose the risky prospect or lotteries with the highest expected utility

(Moscati, 2016).

Modern portfolio theory mean-variance analysis is a deployment of the mathematical

model introduced by Markowitz in 1952 (Francisco dos Santos and Siqueira Brandi, 2017). Authors

explain that modern portfolio theory aims to construct a portfolio of assets whose expected return is

maximized with a given level of risk or volatility, defined as variance of the portfolio. Francisco dos

Santos and Siqueira Brandi (2017) explain that investors tolerate risk differently and some investors

accept lower risk with lower returns and some acts on opposite and intends to take bigger risk for a

larger returns. Resulting in trade-offs if the investors risk aversion is not on the level he wants it to

be. In their work Francisco dos Santos and Siqueira Brandi, (2017) explain the formula of “Modern

portfolio theory” where return of portfolio at the time t (𝑅𝑡) is calculated dividing value of asset at

time t (𝑥𝑡) with same asset in previous time t (𝑥𝑡−1). The formula is written below:

𝑅𝑡 =𝑥𝑡

𝑥𝑡−1

(1)

Authors furthermore exclude other factors necessary to calculate expected return of

portfolio, profitability and name factors like Return of asset, Return of portfolio, transpose matrix of

expected returns, weighting of the asset I (proportion of the asset i in the portfolio) and the matrix

of portfolio weights.

Capital asset pricing model was introduced in financial economics by William Sharpe in

1964 and the earliest model was introduced in 1965 when he collaborated with John Lintner.

(O’Sullivan, 2018).Author explains the basic concept of CAPM model and the formula is presented

below:

𝑟𝑎𝑒 = 𝑟𝑓 + 𝛽𝑎𝑒(𝑟𝑚𝑒 − 𝑟𝑓) (2)

Where 𝑟𝑎𝑒 is expected return on asset so that is determined as the price investor willing to

pay for the asset, 𝑟𝑓 is risk free assets return, 𝛽𝑎𝑒 is expected volatility of the price and 𝑟𝑚𝑒 is

average overall expected return of the market as whole (O’Sullivan, 2018). The Capital asset

pricing model received a lot criticism because of market anomalies and due to that reason a new

asset pricing model was introduced with the name of “efficient market hypothesis”.

Efficient market hypothesis is defined as the market where large number of participants

actively trade in order to maximize profits, with each participant striving to anticipate the future

market price of individual securities (Lekovic, 2018). Lekovic (2018) furthermore introduces the

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empirical model with measuring stock market prices for efficient market hypothesis. It calculates

the return of security at period time t. The formula is presented below:

𝑟𝑡 = 𝑎 + 𝑏𝑟𝑡−1−𝑇 + 𝑒𝑡 (3)

Lekovic (2018) name 𝑎 as expected return on securities not affected by past return, 𝑟𝑡−1−𝑡

as return on securities in period t-1-T, 𝑏 as correlation coefficient between return on securities in

period 𝑡 and return on securities in period t-1-T and 𝑒𝑡 as random error (Lekovic, 2018).

Lastly the concept and logic of arbitrage is explained by already mentioned author Sushma

and Rushdi (2018). In his work authors explains Arbitrage pricing theory as greed of investors

where they try to create return by taking benefit of price fluctuation (Sushma and Rushdi, 2018).

Sushma and Rushdi (2018) in their work explain that according to arbitrage model, investor buys an

asset in the market where it is cheaper and tries to sell it in the market where it is costlier.

These financial theories were used a lot in markets in the last 20-30 years but in the past

years they begin to fail explaining the anomalies that occurred more in markets. Due to that reason

new concept and theory was necessary in order to explain anomalies of market and that’s when

behavioral finance started to rapidly evolve. In the next paragraph concept and evolution of

behavioral finance will be analyzed.

Behavioral finance tries to explain psychological, irrational and biased nature of investors.

This new field of finance is guided by different assumptions and factors affecting the stock market

prices. Behavioral finance assumes that psychological factors are the ones to blame for the market

anomalies and its inefficiency. There exists several theories of the behavioral finance and it started

with the Selden who find out in his research in 1912 that movement of stock prices are dependent to

a considerable degree on the mental attitude of the market participant (Ramiah, Xu and Moosa,

2015). After the Selden work new theories of behavioral finance followed. Ramiah, Xu and Moosa

(2015) name all of them in their work from earliest ones until the newest ones: “Realization utility”,

“Prospect theory”, “Risk aversion theory”, “Disposition effect theory” and etc. “Realization theory”

also takes neuroscience in to the researches of financial player behavior which means that

behavioral finance is evolving in to the higher level of science researching financial human

behavior. Sushma and Rushdi (2018) name two more theories of behavioral finance: “Behavioral

Asset Pricing Model” and “Behavioral portfolio theory” (Sushma and Rushdi, 2018). The first

model names investors as informational and noise traders. Informational traders base their decisions

on CAPM models and noise traders do not use any other already mentioned model in their investing

decisions (Sushma and Rushdi, 2018). The behavioral portfolio theory is based on the concept that

investors divide their portfolio into several mental accounts, each one representing a different goal

(Alles Rodrigues and Lleo, 2018). Authors state that behavioral portfolio theory is not fully rational

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and takes in to account some behavioral biases which lead to market anomalies that traditional

finance theory can’t explain (Alles Rodrigues and Lleo, 2018).

To conclude this section it is possible to say that both of the theories are strongly related.

Traditional theory has some major problems because it has a very strict description about investor

in financial markets and assume that all players are the same and not affected by psychological

factors. Secondly traditional theory is very depended on mathematical calculations and can’t

explain anomalies that occur in the market and that’s why behavioral finance as a different field of

finance theory develops and becomes more popular among researchers. Behavioral finance theory

contemplates traditional finance theory with psychological aspects of investor behavior. One of the

main goals of behavioral finance is to explain those anomalies in financial market, through

psychological aspects of financial player. As it was analyzed the behavioral finance considers

investor as normal meaning that he can make valuation mistakes in his assumptions or could base

his decisions on his emotional statement. Furthermore analyzes of models confirms that both of the

theories are related. We see that two main concepts of behavioral models are strongly related to

CAPM model and Markowitz Modern portfolio theory (Alles Rodrigues and Lleo, 2018).

Furthermore already named “Realization theory” takes neuroscience and human brain activity in

particular as factor of our behavior towards financial decisions. This research focuses on behavioral

finance and how political stability influences the market player decision and their investment

portfolio.

1.2 Traditional and Behavioral Factors Affecting Stock Markets and

Prices

In this section factors affecting the price of stocks will be named. They will be named

separately in order to better understand how different theories relate to different factors affecting the

price of the stocks.

There exist many of factors that affect the price of the stock in traditional finance theory.

As already analyzed the main focus of the traditional finance theory is finding the price of the stock

at the particular time. What affects the price mostly according to researchers will be named in this

paragraph.

Irshad (2017) in his research name four macro-economic factors that effects the price of

the stock market. Irshad (2017) names inflation, export, exchange rate and industrial production.

Zhou, Cui, Wu and Wang (2018) also names most commonly used macro-economic factors

affecting the financial markets and also includes three other factors currency supply, interest rate

and government performance. The last one is really important due to the reason that this work will

specifically be focused on this topic of government performance effect on stock markets. Two other

authors that include more macro-economic factors which effects the stock markets are Asteriou and

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Siriopoulos (2000). They also include the growth rate of real GDP, growth of fixed capital

investment and growth of general stock market index. Very similar to the Asteriou and Siriopoulos

(2000) and Irshad (2017) approach to factors that affect the stock markets also provides Lehkonen

and Heimonen (2015) in their work. They also name GDP per capita, industrial production,

inflation. But they also include turnover, domestic credit, narrow and abroad money growth.

More concentrated analysis on the factors that affect stock markets movements were

analyzed by Hillier and Loncan (2019). They name eight factors that change movement of price on

industrial level. Hillier and Loncan (2019) names sales, equity, debt, liquidity, Tobin’s Q,

Debt/assets ratio, foreigner control, dividends announcement. Zhou, Cui, Wu and Wang (2018) as

micro-economic factors name size of the market, number of investors, investor behavior. After these

factors authors also name dividend yield, payout and leverage rates such as factors affecting stock

prices. Divanoglu and Bagci, (2018) in their research as financial factors name cash flow, risk,

liquidity, return ratios and investment duration. Their perspective on factors affecting the prices of

stocks were observed from the investor view. All of the factors affecting stock prices from

traditional finance theory is named in the table below (Table 1). In the next paragraph behavioral

finance theory factors affecting stock market movements will be named.

Table 1

Traditional Factors Affecting Stock Prices

Category Factor

Macro-

economic

level

Inflation, Export, Exchange rate, Industrial production, Currency supply, Interest

rate, Government performance, BVP growth, Investment growth, Turnover,

Domestic credit, Narrow, Abroad money growth

Micro-

economic

level

Sales, Equity, debt, liquidity, Tobins Q, Debt/assets ratio, Foreigner control,

dividends announcement, size of the market, number of investors, dividend yield and

payout, leverage rates, cash flow, risk, return ratios, investment duration

Raheja and Dhiman (2019) in their work names four categories of human emotions

affecting stock market movements. They name conservatism, Overconfidence, Herding, Regret.

Renu and Christie (2018) names seven other behavioral biases that affect our decision making in

stock markets. They name mental accounting, anchoring, Gambler’s fallacy, Availability, Loss

aversion, Regret aversion, Representativeness, overconfidence.

“Conservatism” is related with people inability to adapt and accept changes (Raheja and

Dhiman, 2019).

“Overconfidence” in Raheja and Dhiman (2019) work is described as people’s

overestimation of futures forecast. Overconfidence is also named in other authors work (Renu and

Christie, 2018). Their description is similar to already mentioned Raheja and Dhiman (2019).

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Deeper understanding about “overconfidence” in investment decision making is analyzed by Raut

and Kumar (2018). These authors name overconfidence as the strongest of all named biases in the

finance world. According to them this bias creates the biggest losses in the investment because of

the people overestimation of accuracy of their information, their successes and capabilities.

Overconfident investors, believing that they possess greater precision on security valuations, trade

too much and thereby lower their expected utility (Raut and Kumar, 2018). Overconfidence was

measured by creating a simulation for the investors that were taken to sample (Raheja and Dhiman,

2019). In their work they analyze previous works and show tendency that in a rise of trend investors

become overconfident.

“Herding” in Raheja and Dhiman (2019) work is outlined as person inability to make

decisions on his own and his investing decisions influenced by other investors. Raut and Kumar

(2018) describes it as an influential behavior of financial players whose action shows animal-like

behavior, where they follow blindly other investors decisions ignoring their intuition and having no

confidence in their decisions. Raut and Kumar (2018) states that these decisions are naturally given

by our nature because humans are sociable and want acceptance and recognition from the society

rather than standing alone.

“Regret” is described as people’s cling on the past loss and its huge impact on future’s

forecasts (Isidore and Christie (2018)

“Mental accounting” which means that people keep winning stocks in one account in head

and losing ones in another even though the portfolio is the same (Isidore and Christie, 2018).

“Anchoring” is the bias that makes investor compares prices of stock from a certain point

of its lifetime (Isidore and Christie, 2018). Raut and Kumar (2018) describes “Anchoring” similar

to Isidore and Christie (2018) and states that anchoring is bias of human beings to compare the price

of item from a certain point and in its life spawn. It creates the illusion for investor or buyer that the

prices should rise to that certain point of anchoring. With anchoring people prefer relative thinking

of the price rather than absolute (Raut and Kumar, 2018).

“Gambler’s fallacy” causes the investors to anticipate the change in the trend of the stock

market depending on the number of years of bullish success or bearish failure (Isidore and Christie,

2018).

In Isidore and Christie (2018) work “availability” is explained as the concentration on

easily available information when making investment decisions while ignoring important and

necessary information. Same characteristics but different name for “availability” was given in Raut

and Kumar (2018) work. They explained it as “availability bias” and explained it as investor

tendency to judge the frequency or probability of an event in terms of how easy it is to think of an

example of that event. In this term individuals information taken in to consideration while making

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investment decision could be less valuable in absolute terms and cause losses after investment

decision.

“Loss aversion” makes people sell winning stocks and keep the losing ones in the portfolio

with the intention that the price of it will go up. Deeper look on the loss aversion provides Bian,

Chan, Shi and Zhou (2018). Bian, Chan, Shi and Zhou (2018) explain that people seeing the rise in

stock price tend to sell them sooner and show risk intolerance while if the same person has losing

stocks he tends to keep it and shows risk tolerance. It is very hard to measure all of named

behavioral factors by quantitative methods.

“Representativeness” is the bias that causes investors take past price as the representative

of the future one (Isidore and Christie, 2018). Raut and Kumar (2018) explains

“representativeness” in deeper manner and explains it as bias in which financial player under

circumstances of uncertainty in which financial player makes a judgement about company based on

the similar in essential properties to its parent population and reflects the salient features of the

process by which it is generated. Secondly Raut and Kumar (2018) explain that because of that

financial players select the companies based on their recent returns, popularity, type of management

and etc.

“Endowment bias” is the bias when people tend to have sentimental feelings for a property

and tend to overprice it because of that emotion (Sushma and Rushdi, 2018).

Sushma and Rushdi (2018) in their work not only describes the psychological aspects of

human behavior that could influence the investment decisions and market movements but also

groups all of the already mentioned biases in two categories: cognitive biases and emotional biases.

Cognitive biases are anchoring and adjustment, framing, conservatism, availability, mental

accounting (Sushma and Rushdi, 2018). Emotional biases in his work are endowment bias, loss

aversion, optimism and status quo. This categorization model was firstly introduced by another

author, Pompian in 2011 (Sushma and Rushdi, 2018). Another categorization for these

psychological aspects was introduced earlier in 2000 by scientist Hersh Shefrin (Sushma and

Rushdi, 2018). He also categorizes these biases in two categories, one named “Heuristics”

(overconfidence, anchoring, adjustment, reinforcement learning, excessive optimism and

pessimism) and other “Frame dependent biases” (narrow framing, mental accounting and the

disposition effect) (Sushma and Rushdi, 2018).

Another aspect of psychological factors affecting investor decision making is based on the

cultural differences. As an example could be Zhou, Cui, Wu and Wang (2018) work in which they

tried to evaluate what cultural differences has on investing behavior. In their findings results

showed that countries with smaller cultural difference have similar level of volatility and different

cultural dimensions have different influence on volatility of country financial markets. Other

15

research on cultural aspect of decision making for investments were researched by Divanoglu and

Bagci (2018). Authors states that environmental factors have an impact on investors decision

making (Divanoglu and Bagci, 2018). They name environmental factors such as family, friends,

close environment, socio-cultural environment (Divanoglu and Bagci, 2018). Authors explain the

reasons behind it and that could be because of the lack of knowledge in investing, seeking the

approval of others and etc., similar to herding or information cascades (Divanoglu and Bagci,

2018). All of the psychological aspects of human behavior were shown in the table below (Table 2).

Psychological factors in the table 2 are separated in two categories: Cognitive bias and emotional

bias.

Table 2

Psychological Factors Affect Stock Prices

Cognitive bias Emotional bias

Anchoring, Adjustment, Framing,

Conservatism, Availability, mental accounting,

Gamblers Fallacy, Representativeness

Endowment bias, Loss aversion, optimism,

status quo, Overconfidence, Herding, Regret.

Lots of factors affect stock markets and its prices, indexes. When measuring the stock

market movements from behavioral finance theories perspective we see that factors affecting stock

prices movement are based on psychological aspects of human behavior. From analyzes of previous

authors works it is possible to say that many different factors affect human behavior deciding their

investing decisions starting from misinterpretation of information to emotional valuations of stock.

Decision could be influenced by environmental, cultural aspects of specific area the investor is

located. Speaking of factors affecting the decision making and movement of financial markets from

micro-economic level we have firm size, sales, equity, debt, liquidity, Tobin’s Q, Debt/assets ratio,

foreigner control, dividends announcement. In macro-economic level we have factors like inflation,

export, exchange rate, industrial production, grow rate of real GDP, growth of fixed capital

investment and growth of general stock market index. These factors are more easily measurable

then previously mentioned psychological ones and also provides quite reliable information on stock

prices and movements. Nevertheless there could be a lot of more factors that affect stock market

indexes but these already named ones give the perception of how dynamic and easily influenced

stock market is. In the next section political instability and its effect to stock markets will be

analyzed.

1.3 Financial Players’ Behavior Influenced by Political Uncertainty

In this section the effect of political uncertainty, its factors will be analyzed and explained.

Firstly, even though diversification of portfolio with foreign country assets shows larger gains of

portfolio, investors do not tend to invest that much in foreign country assets due to the fact of

16

political instability (Smimou, 2014). After deeper understanding and research in to why investors

tend to keep their investment in the home country authors find out that after political instability

other factors follow, that would be taxes system of that country, border controls and political and

social trends (Smimou, 2014). Some of the authors analyze what effect has political instability on a

sector level (Moshi and Mwakatumbula, 2017). Authors analyzed the effect of political stability to

telecommunication market and find out that political instability negatively effects

telecommunication market. Authors provides with the necessity to take a deeper look in political

instability on sector level, because in some sectors it could provide with the benefits (Moshi and

Mwakatumbula, 2017).

Political instability and uncertainty as we can see from already analyzed researches has as

huge impact on investor behavior and their psychological state. Political instability or uncertainty is

researched by the behavioral theory scientists that concentrate on the influence of emotions and

cognitive biases on people judgments and decision making (Soltani, Aloulou and Abbes, 2017).

These authors name political instability as factor that could make financial markets collapse.

Wisniewski (2016) in his work name political uncertainty as the risk that is created by the unstable

political environment which are reverberate in financial markets and diminish shareholder wealth.

Furthermore it is necessary to understand how political uncertainty manifest in nowadays

society or what kind of factors, events from political point of view drives the markets. Mnif (2017)

in his work name political uncertainty factors that drives financial markets. He names events like

presidential elections, terrorism attacks, military invasions/wars and civil overthrows of local

government. Similar to these uncertainties were named by Wang and Boatwright (2019) in their

work authors name it as political shocks or policy changes. Wang and Boatwright (2019) don‘t split

political uncertainty in to the deeper categories and name it as two already mentioned but in their

work the political uncertainty is treated as in Mnif (2017) work. Wisniewski (2016) in his work like

other authors names three already mentioned political uncertainties which are presidential elections,

terrorism attacks, civil wars and wars. Nevertheless, Wisniewski (2016) in his work name fourth

political uncertainty that effect stock markets which is called „political communications“. „Political

communications” is the factor that focuses on political speeches and communiques that could

possibly influence movements of stock market. Irshad (2017) specifies in his work the political

uncertainty factors and names six of them. He names strikes, assassinations, riots, demonstrations,

government longevity and government change. He specifies them more deeply and more

specifically but they all could be grouped in the already named categories that are terrorism, war,

civil war, and elections.

After analyzing other authors work it is possible to conclude that political stability,

certainty is very necessary to the development of countries and stability of their financial systems.

17

As we already saw in some of the authors works political stability is one of the key factors affecting

the financial markets and its instability could influence collapse of the financial market.

Furthermore deeper analysis showed that political uncertainty lowers the profits of investors which

lead to larger investment in domestic countries and foreign direct investment loss. Lastly analyzes

of previous authors works helped to group and name key factors which affect the political

uncertainty and stability which are terrorism, civil wars, wars, elections and government

communications. In the next paragraphs models for evaluating event effect for financial markets and

“BREXIT” as the political uncertainty and its effect to financial markets from previous author

works will be analyzed.

1.4 Empirical Models for Events Effect Estimation

In finance abnormal returns of market, volatility can be interpreted as political or financial

uncertainty and after it, it becomes the primary focus for many investment decisions and portfolio

management. Too much of volatility or movement in daily returns is a sign of unstable economy

and high risk of investment while the opposite could be said about low volatility and returns. In

order to measure the volatility and returns of the markets of domestic or even global the various

number of methods are applied.

First and mostly related to original “Event-Study” Method is analyzed by Chen (2004).

“Event-study” model is based on efficient market hypothesis. This hypothesis states that as new

information becomes available, it is fully taken into consideration by investors assessing its current

and future impact. That means that investors immediately reassess individual firms and their ability

to withstand potential event (Chen, 2004). According to researchers if investors react favorably to

event we should expect positive returns and if investors reacts negatively to event we should expect

negative returns (Chen, 2004). “Event-study” model tries to identify the abnormal returns to firms

from a specific event (Chen, 2004). Furthermore, “Event-study” tries to capture the influence of that

event in the specific period of time the event occurred (Bonchev and Pencheva, 2017). In many

research’s event period is selected different and do not have strict rules that should be applied

selecting the period (Bonchev and Pencheva, 2017). Bonchev and Pencheva (2017) in their work

name examples of time-periods, for example (-1, 1), (-3, +3) and etc. The same rules are applied for

“estimation window”. It is up to researchers to select what size of "estimation-window" he selects

but it is important and suggested to select at least 250 days before the major event day.

Some authors try to merge empirical models based on their research logic and develop

modified approach to “Event-study” method. In the present time the “Event-study” method is

becoming more and more complex and Shahzad, Rubbaniy, Lensvelt and Bhatti (2019) represent

the supplemented version of this model. Shahzad, Rubbaniy, Lensvelt and Bhatti (2019) update the

model with more specific control variables in order to better evaluate the effect of events. The

18

control variables depend on the hypothesis and questions in the methodology and can variate from

micro to macro level. In Shahzad, Rubbaniy, Lensvelt and Bhatti (2019) case they name “Friday

effect”, “Halloween effect”, “January effect”, “Mark Twain effect”, “Turn of the month effect” and

some control variables on firm level like “Market value”, “labour dependency”, “foreign sales” and

were the company against the “Brexit” on votes. Next work that used different control variables

were done by Davies and Studnicka (2018). Davies and Studnicka (2018) applied “Event-study”

method and estimated effect of “BREXIT” on firm level and were trying to estimate that companies

who were exposed in UK and EU were doing worse. Based on their hypothesis and assumptions

their control variables were used from firm level. Davies and Studnicka (2018) used “industry-

specific exchange rate depreciation” as control variable in their research. Oehler, Horn and Wendt

(2017) same as Shahzad, Rubbaniy, Lensvelt and Bhatti (2019) used Market Capitalization, Foreign

sales as control variables in their research. Oehler, Horn and Wendt (2017) used industrial sector as

additional control variable in their work stating that different industries would be affected different

due to the “BREXIT”. Even though the control variables might differ in different scenarios but the

main focus is still the same to catch the anomalies in the daily, hourly and etc. returns in specific

markets. Control variables are important in this method in future researches because as many

authors work show it is hard to capture whether the observed event has an effect for the financial

markets or other external, unrelated factors might influence the results of indexes. Morales and

Andreosso-O’Callaghan, (2019) in their work explain as one of the limitations of their work the

necessity of larger group of control variables because results might have been affected also by other

factors.

Secondly authors use “Event-study” method to estimate not actual prices and abnormal

returns of the market, but volatility of the market. “Event-studies” whom measure volatility of

markets help us track the negative and positive outcomes of those events. Morales and Andreosso-

O’Callaghan (2019) in their work state that modeling such scenarios help us to understand the

volatility’s persistence and clustering of these kind of events. High volatile markets suggest that

investing in them would be a lot riskier than in low volatility markets and that should be taken in to

consideration when planning your investment portfolio. In econometrics to catch the volatility

Autoregressive Conditional Heteroskedastic (ARCH) and Generalized Autoregressive Conditional

Heteroskedastic (GARCH) models are used. Autoregressive Conditional Heteroskedastic (ARCH)

were introduced and firstly used by Robert F. Engle in 1982 (Morales and Andreosso-O’Callaghan,

2019). Morales and Andreosso-O’Callaghan (2019) explains in their paper that after the appearance

of ARCH method the GARCH were introduced whom were improved by Bollerslev in 1986.

GARCH model overcome the limitations of the ARCH model which was based on past sample

variance (Morales and Andreosso-O’Callaghan, 2019). After “ARCH” and “GARCH” methods

19

appearance in finance world other future scientists improved the models so the others methods like

“EARCH”, “GJR (TARCH)”, “GARCH-M” were introduced in future time (Morales and

Andreosso-O’Callaghan, 2019).

Another model for estimation is named “copula functions” (Aristeidis and Elias, 2018).

These models according to Aristeidis and Elias (2018) are usually employed by authors to depict

tail dependence in financial time series. Additionally, the models are capable of detecting non-

normality and fattailedness in stock exchange markets. This model is employed by Aristeidis and

Elias (2018) in their work and they try to evaluate the announcement of “article 50” and negative

effect for the UK and other countries market.

Some methods to estimate the effect of the event takes not a stock market indexes but

foreign exchange markets. Foreign exchange markets and stock prices are closely related and have

been commonly utilized by fundamentalist investors to predict future trends (Bashir et al., 2019).

Given the event uncertainty to market, researchers analyze its effect through Foreign exchange

markets.

Bashir et al. (2019) applies and explains detrended fluctuation analysis (DFA) and

detrended cross-correlation method to analyze the relationship between stock markets and foreign

exchange rates while influenced by the given event effect to them. According to theory analyzed by

Bashir et al. (2019) if the exchange rate is competitive it will affect the trade of the country and its

economy increasing its product profitability and price while on the opposite note depreciation will

decrease the profitability, firm value and their share prices. Which means that changes in currency

will suggest the effect of the event (Bashir et al. 2019). As with other methods this one also takes an

“event-window” and “estimation-period” which is also do not have a strict rule for how long period

should last but also considered to be not as small as few hundred days for major events. To

conclude, this method tries to capture the effect of the events not directly through the markets but

through the exchange rates of countries currency. This method also benefits not only on the

portfolio management and decision making but could also be applied by making and deciding on

financial policies of the country which could benefit efficiently in controlling the situation the

events cause.

1.5 Overview of Previous Research on “BREXIT” Effect to Financial

Market

On the 23rd of June 2016, UK citizens voted to leave European Union also known as EU

and this event was named “BREXIT”. “BREXIT” had an immediate effect on stock markets.

Guedes, Ferreira, Dionisio and Zebende (2019) in their work analyzed how “BREXIT” affected EU

stock markets. Their results showed negative effects in most of the cases except Malta, Bulgaria,

Slovenia. Also Guedes, Ferreira, Dionisio and Zebende (2019) results show that UK indexes are

20

less integrated with the other EU countries and that it prevent greater well-being of countries. But

they conclude one plus of it that if some asymmetric shock occurs they could mitigate it through

pound sterling exchange rate.

Other authors work analyzes “BREXIT” effect through the view of UK companies

(Oehler, Horn and Wendt, 2017). Authors analyze “BREXIT” effect on daily returns of companies

depending on the internationalization level. They assume that with higher in internationalization

level companies should have less negative effect to their prices due to the “BREXIT”. Oehler, Horn

and Wendt (2017) in their work accepted the main hypothesis and showed that companies with

more domestic sales experienced larger losses.

The third research about “BREXIT” effect on stock markets of UK was done in two

phases: pre-referendum and post-referendum (Shahzad, Rubbaniy, Lensvelt and Bhatti, 2019). Their

results were similar to previous mentioned authors in this paragraph, they found out that results

were negative at first, but after the referendum their results differ to positive which means that at

some point some investors started to see positive effect of “BREXIT”.

Fourth research work on this topic was also analyzing two periods of “BREXIT” (Bashir et

al., 2019). Their results showed the same patterns in exchange and stock market rates as in

(Shahzad, Rubbaniy, Lensvelt and Bhatti, 2019) work. It showed immediate negative result at first

phase of “BREXIT” or as it called pre-referendum and some positive patterns in post-referendum

phase (Bashir et al., 2019). Authors also analyzed not only UK stock market parameters but also

correlation between other four largest Europe region economies and find out immediate negative

results between UK and Germany, France, Netherlands.

Completely different result in their work got Ramiah, Pham and Moosa (2017). Authors

estimated the effect of “BREXIT” on the industrial level and find out that in total the “BREXIT”

has a negative result for many industries and the effect of “BREXIT” is negative especially for the

UK market. Their results showed a wider perspective for the UK market on firm level and shows

that in longer-period the UK is making losses for its market.

Another author work estimating effect of “BREXIT” to financial markets were written by

Boulton and Bacon (2018). In their work they analyzed the effect of “BREXIT” announcement that

UK leaves EU on 23rd of June in 2016 to ten firms that are exposed in Britain and the European

Union markets. Boulton and Bacon (2018) selected to estimate the period of 180 days before the

event and 30 after. For this estimation they selected previously mentioned event method and their

results showed that 5 days until the announcement of news indexes spiked up because of the

optimistic emotions of investors that UK won’t leave EU. Emotions of investors were positive due

to the pools released on June 20th and answers showed declining chances of “exit” of the UK.

Nevertheless 3 days until “BREXIT” stock prices significantly decreased because pools didn’t work

21

and the exit was inevitable. Boulton and Bacon (2018) work showed very similar results to Ramiah,

Pham, and Moosa (2017) work and showed declines in stock prices on the event days and after it.

But as many previous authors work showed the prices spikes up after a few weeks after the

referendum of the “BREXIT” (Shahzad, Rubbaniy, Lensvelt and Bhatti, 2019). The same results are

reflected in Boulton and Bacon (2018) work and the prices rises after 21st day of the referendum.

Table 3

“BREXIT” Effect To Financial Markets

Authors name Results

Industrial – company level researches

Oehler, Horn and

Wendt (2017)

United Kingdom companies with lower internalization level experienced

larger loses than the ones with higher internalization.

Ramiah, Pham, and

Moosa (2017)

Ramiah, Pham, and Moosa (2017) also find out negative results on

industrial level for the UK market.

Boulton and Bacon

(2018)

10 biggest stock indexes were tested in these authors work. Prices

significantly increased 5 days until referendum, huge loses followed 3 days

until referendum and only became positive a few weeks after referendum

Financial market level researches

Shahzad,

Rubbaniy, Lensvelt

and Bhatti, (2019)

Bashir et al.,

(2019)

Loses in financial markets in pre-referendum phase and gains in post-

referendum phase. Also Bashir et al., (2019) found immediate negative

correlation between UK and Germany, France and Netherlands.

Guedes, Ferreira,

Dionisio and

Zebende (2019)

Negative effect for UK and other more related countries to the UK. Results

didn’t change or change insignificant were in Malta, Bulgaria and Slovenia.

Source: compiled by author based on the research referenced in the table

All of the results of previous authors work about “BREXIT” effect to financial markets of

United kingdoms is summarized in the table before (Table 3). All of the researches are categorized

in two categories: industrial and financial level. As we can see from the results on industrial level

United Kingdom experienced loses according to all researchers. The results do not differ much and

in financial level showing that financial markets experienced loses due to the “BREXIT”.

It is also very important to analyze what effect “BREXIT” had on other countries who

were more or less related to UK and were one of the trading partners. Abraham (2018) did a

research what effect “BREXIT” had on New Zealand stock market. Being the close trading partner

to UK, New Zealand should also experience negative results regarding negative news about UK and

22

“BREXIT” in particular. Even though analyzes suggests that “BREXIT” could open up new

business opportunities to New Zealand in the long term because New Zealand is not so dependent

on UK as it was in 1970 (Abraham, 2018). Abraham (2018) as many others authors mentioned

earlier did an event study estimation to answer what effect has UK’s announcement to leave EU.

Abraham (2018) analyzed 50 largest, eligible stocks listed on the main board of New Zealand stock

market index (NZX 50 Index). He selected the “event-window” of 40 days. 20 days before the

referendum announcement and 20 days after. Abraham (2018) results showed significant negative

results in period of time of 16 days before the announcement and 16 days after. In this period of

total 32 days the stock market index decreased while after it become positive and started rising.

That means that compared to Boulton and Bacon (2018) results Abraham (2018) results started to

show significant results much earlier than in UK which means that New Zealand reacted more

significantly to financial uncertainty due to the “BREXIT”. New Zealand’s market got better and

positive results faster than UK’s in post referendum phase. That is probably because of the

investment movement from UK to other countries (Abraham, 2018).

Another work analyzing the effect of “BREXIT” to other countries stock market indexes

were calculated by Madhavi and Reddy (2018). These authors were estimating the effect of

“BREXIT” on India’s stock market and its different industry sectors. Authors estimated “BREXIT”

influence for the India’s industry sectors using “event-study” method. Madhavi and Reddy (2018)

used “ARCH” and “GARCH” methods for estimating volatility of those sectors. They were

interested to see which sectors would be affected mostly due to the “BREXIT” referendum. Results

of their research showed significant effect of “BREXIT” to volatility of markets. But the results

were significant in volatility before and after the actual event date. On the event date volatility

didn’t change significantly which means that market reacts before the event and after it. Lastly

authors describe the limitations of their research explaining that longer period is necessary to

estimate the effect of the “BREXIT” because many other external factors affect stock markets and

only longer period could evaluate the effect of “BREXIT” more precisely.

Third research made regarding “BREXIT” effect to other country economies was made by

Morales and Andreosso-O’Callaghan (2019). These authors analyzed how “BREXIT” effected

China’s stock market. Authors estimate the abnormal returns and volatility of mainland China,

Hong Kong and Taiwan’s stock markets. Results showed that these stock markets do not exhibit

any abnormal returns due to the “BREXIT” events. The same goes with the estimation of volatility

to these markets, the effect of “BREXIT” is insignificant to volatility (Morales and Andreosso-

O’Callaghan, 2019). China’s stocks markets do not seem to be panicking and overreacting to recent

major political events that happened in the EU (Morales and Andreosso-O’Callaghan, 2019). Of

course, China should be monitoring major events happening in the world because of the trading

23

possibilities in EU or other regions but as the result show political events are not one of them. All

researches findings summarized are presented in the table below (Table 4).

Table 4

“BREXIT” Effect To Other Financial Markets

Authors name Results

Abraham

(2018)

Negative Results in New Zealand’s financial markets 16 days before the

announcement of “BREXIT”. The results got positive only 16 days after the

“BREXIT”

Madhavi and

Reddy (2018)

Authors tested volatility of India’s financial market due to the “BREXIT”.

Results were significant and showed differences in volatility due to the

“BREXIT”. Volatility changed days before the event and after it, but showed no

significant effect on actual date of event.

Morales and

Andreosso-

O’Callaghan

(2019)

Authors analyzed how Chinese stock market were affected due to the “BREXIT”.

No significant effect to Chinese stock market was showed during the period of

“BREXIT”. Authors tested the returns and volatility of the market and didn’t find

and significant results to it.

Source: compiled by author based on the research referenced in the table

To summarize all chapter about theoretical aspects of finance theories, models and political

stability and their effect on financial markets this can be stated. Traditional finance theory is finance

theory based on the logic that investor is rational, risk-averse and aimed to maximize “expected

utility” at every decision he makes. Behavioral finance theory is finance theory that considers

investor not as ration but as “normal” which means that investors can make mistakes in their

calculations. Behavioral finance theory started to develop more rapidly after traditional finance

theory failed to explain financial market anomalies like market bubbles and etc.

Furthermore analyzes of previous researchers determined that stock prices movements

could be influenced by large number of factors from macro-economic to micro-economic levels.

Macro-economic factors such as inflation, export, exchange rate, industrial production, currency

supply, interest rate and etc. were presented in this work. Micro-economic factors affecting prices

of stocks named in this research were sales, equity, debt, liquidity, Tobins Q, debt/assets ratio,

foreigner control, dividends announcement, size of the market and etc. Psychological factors

affecting stock prices named in this work were based on two categories: cognitive bias and

emotional bias. Cognitive bias category consists of anchoring, adjustment, framing, conservatism,

availability, mental accounting, gamblers Fallacy, Representativeness factors. Emotional bias

category consists of endowment bias, loss aversion, optimism, status quo, overconfidence, herding,

regret factors.

24

Analyzes of how political stability affects the financial markets it can be stated that

political stability, certainty is very necessary to the development of countries and stability of their

financial systems. Political stability is one of the key factors affecting the financial markets and its

instability could influence collapse of the financial market. Political uncertainty lowers the profits

of investors which lead to larger investment in domestic countries and foreign direct investment

loss.

Lastly to evaluate the effect of such events that increased political instability of country or

etc. and their effect to financial markets models such as “event-study”, GARCH, copula functions

and detrend fluctuation analysis were presented. “Event-study” helps to capture how event affect

the actual prices of markets while GARCH focuses on volatility of the market. Previous works

estimating “BREXIT” event effect for financial markets determined that UK financial market

experienced larger loses in pre-referendum phase and only recovered a few weeks after it (Shahzad,

Rubbaniy, Lensvelt and Bhatti, (2019). Bashir et al., (2019) and Guedes, Ferreira, Dionisio and

Zebende (2019) also find out negative effect between UK and other EU countries. Countries with

strong trade relations with UK also experienced negative effect for the financial markets. In this

case New Zealand and India experienced it. On industrial level companies with lower

internalization level experienced larger loses than the ones with higher internalization. Most of the

UK economy sectors experienced loses due to the “BREXIT” vote.

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II. “BREXIT” NEWS EFFECT FOR “LONDON STOCK

EXCHANGE” AND INDIVIDUAL COMPANIES

METHODOLOGICAL JUSTIFICATION

In the second chapter of the work research methodical part is analysed. In the first part

relevance and aim of the research will be analysed and stated. Furthermore the hypothesis of the

research based on previous works will be constructed. Lastly the empirical model of the research

and data necessary to carry out the research will be presented.

2.1 Relevance and Aim of the Research

In literature review traditional and behavioral finance theories were analyzed. What was

noted from the literature review was that not everything can be estimated using traditional finance

models. In some cases traditional finance models lacks the estimation and can’t get a grasp of

psychological factors affecting the decision making by financial players. For example, traditional

finance models lack evaluations of human emotions such as fear, optimism, overconfidence, mental

accounting, anchoring, Gambler’s fallacy, Availability, Loss aversion, Regret aversion,

representativeness and etc. Emotions affecting the decision making in financial markets previously

analyzed in the literature review should be put in the estimation models in order to better capture

the effect of the event to financial markets.

Secondly, greater amount of the events regarding “BREXIT” is necessary to analyze to

better capture the effect of the “BREXIT” to United Kingdom financial market because all of the

previous researchers were only focused on the “BREXIT” announcement day after the vote on 23rd

of June in 2016. As the previous researches showed “BREXIT” announcement had a significant

effect before and after the referendum. Results showed significant negative effect for most countries

financial markets except for China, Malta, Bulgaria and Slovenia. Markets only recovered a few

weeks after the “BREXIT” announcement. Which indicates that markets estimated the real effect of

the “BREXIT” announcement and recovered to real value of it.

To begin with it is necessary to understand how political instability and uncertainty affect

financial markets. Soltani, Aloulou and Abbes (2017) in their work name political instability as

factor that could make financial markets collapse. Wisniewski (2016) in his work analyze political

uncertainty as the risk that is created by the unstable political environment which are reverberate in

financial markets and diminish shareholder wealth. Mnif (2017) in his work name political

uncertainty factors that drives financial markets. He names events like presidential elections,

terrorism attacks, military invasions/wars and civil overthrows of local government. Other authors

named after describes similarly what could be treated or described as political instability,

26

uncertainty. Afterwards Wisniewski (2016) in his work name another political uncertainty that

effect stock markets called „political communications”. Wisniewski (2016) describes political

communications as news made by local government that could influence financial markets. Political

communications are separated from presidential elections, terrorism attacks and others because of

the impact on the financial market. For example terrorism attacks, military invasions and others

could increase political instability in country and have a strong negative impact for financial

markets while political communications could influence it on opposite way and have a positive

effect if the news decreases political instability in country.

The previous paragraph identified different political events that influences financial player

behavior in financial markets. Previously named political events cause fear, pessimism and regret.

Negative emotions could highly decrease total investments in local countries financial markets and

could cause financial crash of the market. Investors do not tend to invest that much in foreign

country assets due to the fact of political instability (Smimou, 2014).

Today the world face a new kind of political instability also known as “BREXIT”. In 2016

British referendum decided to leave European Union and this event were not taken lightly by the

financial markets all over the world and United Kingdom itself. As the previous authors works

showed United Kingdom experienced losses due to the “BREXIT”. Oehler, Horn and Wendt (2017)

in their work states that companies with lower level of internationalization experienced bigger loses

than the ones with higher level of internationalization. Ramiah, Pham and Moosa (2017) analyzed

the effect of “BREXIT” on industrial level and find out that consequences will be much higher in

the future than it was in 2016. “BREXIT” also affected other countries and their economies. For

example close trading partners like New Zealand and India experienced major changes in the

markets due to the “BREXIT”. New Zealand started to experience loses much earlier than UK

which states that New Zealand financial markets are more elastic to uncertainty and instability than

UK. India’s financial markets due to the “BREXIT” experienced large changes in volatility during

the period of pre-referendum and post-referendum, meaning that UK political well-being has a

significant role on India’s financial market.

Various researches makes an insightful conclusions about what effect “BREXIT” will have

on market returns. Additionally, it is relevant to understand and how later news regarding

“BREXIT” affects the financial market. That is why the main purpose of this research will be

capturing how “BREXIT” news affects the United Kingdom markets returns. Analyzing what

financial behavior is influenced by the “BREXIT” could help to local and foreign governments and

investors react to situation more rationally.

After analyzing the effect of “BREXIT” news to United Kingdom financial market it is

necessary to take a deeper look on how it affects individual companies in United Kingdom. Oehler,

27

Horn and Wendt (2017) and others analyzed companies with higher and lower levels on

internalization and find out that announcement of “BREXIT” had a larger negative effect for

companies with lower internalization level. Ramiah, Pham and Moosa (2017) in their work

analyzed the effect of “BREXIT” on industrial level and their results showed that the banking,

travel and leisure sectors were experienced largest loses compared to other sectors. Boulton and

Bacon (2018) estimated how “BREXIT” affected ten firms, traded on New York Stock exchange,

that are also heavily linked with the United Kingdom market. Their results showed negative return

on all of them regarding “BREXIT”.

To conclude the reasoning of research it is necessary to understand whether the “BREXIT”

news has an effect and influence to financial markets in a longer time perspective because

according to previous works findings it has a significant effect to financial markets worldwide.

Another reason for this research is that it is also necessary to understand whether “BREXIT” news

announced later after the referendum to leave EU affected the markets negatively like it did at first.

As it was already mentioned “BREXIT” is the event like no others and understanding its effect

could give an insightful notes towards future researches with similar events. The “BREXIT” for this

research is classified as political instability, uncertainty and fear rising event that influences

negative financial markets reaction. In this research effect of “BREXIT” news will be analyzed to

United Kingdom financial markets and individual companies in different industrial sectors listed on

United Kingdom financial market. In existence of negative effect for financial markets in UK

financial markets the measures for future financial policy decisions should be taken because

political instability could have a large negative impact on financial markets and could even cause

financial collapses of it.

The aim of this research is to analyze how “BREXIT” news effects and influences

United Kingdom financial market, individual companies and various sectors.

2.2 Research Hypothesis

In this chapter research hypothesis will be formulated. Formulation of hypothesis is based

on previous research exploring “BREXIT” effect to financial markets. First of all it is important to

assess if later news regarding “BREXIT” had a significant effect for United Kingdom financial

market. Furthermore, what is important to evaluate whether the news regarding “BREXIT” still had

a negative influence to United Kingdom financial market as it did at the time around referendum for

“BREXIT”. In the previous researches authors estimate the effect of one event and do not include

others that happened later on. Ramiah, Pham and Moosa (2017), Oehler, Horn and Wendt (2017),

Boulton and Bacon (2018) and other researchers named in previous chapters only estimate the

announcement of “BREXIT” day which only estimates one event effect for the financial market.

Due to this fact this research will evaluate and focus on the effect of all other key dates of

28

“BREXIT” happened later on after the referendum to United Kingdom financial market. As it was

already mentioned in the previous chapters the effect of “BREXIT” announcement for the United

Kingdom financial market was negative. Oehler, Horn and Wendt (2017), Ramiah, Pham and

Moosa (2017), Boulton and Bacon (2018) find out negative results on industrial level and estimated

the effect of “BREXIT” announcement to different sectors. Guedes, Ferreira, Dionisio and Zebende

(2019), Shahzad, Rubbaniy, Lensvelt and Bhatti, (2019), Bashir et al., (2019) in their work analyzed

the effect of that event on a country level to largest stock market indices. Results showed negative

returns of markets in the estimation period of the event. Based on that logic first hypothesis of the

research were formulated:

H1: “BREXIT” news have a significant negative effect on London stock exchange.

The main United Kingdom financial market is London Stock Exchange. In this research

London Stock Exchange index price was taken as the representativeness for the whole countries

financial market. The effect for daily returns due to the “BREXIT” news will be calculated to

London Stock Exchange. Guedes, Ferreira, Dionisio and Zebende (2019), Shahzad, Rubbaniy,

Lensvelt and Bhatti, (2019), Bashir et al., (2019) in their works estimated the effect of “BREXIT”

referendum to price changes of market and due to this reason the changes in prices will be taken as

markets reaction to “BREXIT” events. Hypothesis will be accepted if at least half of the events will

have a significant negative effect in at least one of the estimated “event-windows”.

The second hypothesis is based on Boulton and Bacon (2018) research. Authors estimated

the effect of “BREXIT” announcement to 10 biggest companies traded on the NYSE (New York

Stock Exchange) who is very closely related to the United Kingdom financial market. Authors

estimate how much “BREXIT” affected daily returns on sixty days period around the

announcement day. They calculated the abnormal returns for 30 days before the event and 30 days

after the event. Their results showed growth in returns on the fifth and fourth day until vote

following with large decline in returns on the last three days until the vote. Returns bounced back

only on 21st day after “BREXIT” vote. Based on that logic second hypothesis were formulated:

H2: “BREXIT” news have a significant negative effect on individual companies in United

Kingdom listed on London Stock Exchange.

To test this hypothesis twenty-five companies with largest market capitalization, which

were listed on London stock exchange, were analyzed. Effect for the returns due to the “BREXIT”

news will be calculated for them and after that the hypothesis will be accepted if at least half of the

events will have a significant effect for at least half of the companies in at least one of the estimated

“event-window”.

Furthermore, it is important to analyze which sectors are affected by the “BREXIT” news

more than the others. Ramiah, Pham and Moosa (2017) estimated how “BREXIT” vote affected

29

different United Kingdom sectors. Ramiah, Pham and Moosa (2017) based on discussions in

“Financial Times” rise the hypothesis that financial and banking sectors should experience negative

returns due to the “BREXIT” vote. Their results approved this hypothesis and those two sectors

experienced negative returns of 15% during event period. Other sectors affected most were leisure

and travel and Oil and gas producers. Based on these authors work the third hypothesis will be

tested:

H3: “BREXIT” news have a stronger negative effect to financial sector companies

compared to other sector companies.

As it was already mentioned there are twenty-five companies from different sectors with

largest market capitalization listed on London Stock Exchange whose daily returns affected by

“BREXIT” events will be calculated in order to understand whether “BREXIT” events happened

later on had any effect for them. That list contains seven financial sector companies out of twenty-

five. If at least fifty percent of financial sector companies will have a negative abnormal returns in

at least half of the “BREXIT” events the hypothesis will be accepted.

Main focus of this paper to analyze how “BREXIT” events effects returns of UK financial

markets and individual companies. It is known that “BREXIT” raises a lot of questions what will

happen not only to the economy of UK but the whole European Union. “BREXIT” is the event like

no others happened in the last decades which suggests that it is necessary to understand how it will

affect the financial markets so that in future policy changes or suggestion for investors could be

taken in advance of those events.

Nevertheless these kind of events that creates political instability and uncertainty are

known for increasing “Herding” financial behavior in financial markets. Vidanalage, Shantha

(2019) in their work analyze “herding” behavior in the Colombo Stock exchange in Sri Lanka.

Authors analyze the periods of uncertainty in Sri Lanka and presence of “Herding” in the market.

They test how “Herding” behavior changed during different periods of Sri Lanka existence.

Vidanalage, Shantha (2019) state that in 2000-2009 years period the herding should have been

higher and strongly evident due to the fact of political instability, uncertainty and Civil War. Indars,

Savin, Lubloy (2019) in their work states that political turmoil strongly influences the financial

markets because it is strongly related to uncertainty of country. Indars, Savin, Lubloy (2019) also

state that “herding” is highest in the financial uncertainty times and in crises periods, giving the

examples of Asian crises in 1997-1998, subprime crisis in United States of America and etc.

According to them “herding” should be stronger in those periods of time. Which formulates the next

hypothesis for the research:

H4: “BREXIT” news trigger financial herding behavior in London stock exchange

30

Hypothesis will be accepted if at “event-window”, changes in individual company stock

volume will be significant at least in fifty percent of the “BREXIT” events for half of the

companies.

After argumentation and formulation of hypothesis that needs to be tested in order to

understand better if “BREXIT” events have an effect for United Kingdom financial market the

empirical model needs to be adapted and constructed.

2.3 Logic and Argumentation of Research Model

In order to test the hypothesis that was named in the previous chapter it is necessary to

analyze the method and logic of the research. First of all for this research the “event-method” will

be used in order to accept/reject hypothesis. The event study methodology applied here is based on

the efficient market hypothesis (Chen, Siems 2004). Efficient market hypothesis assumes that as

new information stemming from an important unpredictable event becomes available, market agents

will take the information into account and will re-evaluate values of individual firms given

economic, environmental, political, social and demographic changes that the exogenous event might

bring about. The power of this methodology is its ability to trace such “abnormal” changes, because

it follows the general valuation of many investors that (re)examine all the available data for the

estimation of the market value of each traded stock (Schwert, 1981). Based on logic that the main

purpose of the research is to test the actual returns of financial markets influenced by „BREXIT“

news „event-study“ fits more rather than other methods because other methods such as GARCH,

EGARCH and others estimate the volatility of the market rather than returns of financial market.

Secondly, „event-study“ model fits the estimation of hypothesis more than the others due to the

reason that research is interested in individual companies and their returns and how „BREXIT“

news affect the different sectors. Different models estimating volatility won‘t be able to answer

whether the effect for some sectors such as financial like in the H3 are more affected than the

others. Lastly GARCH and others models can‘t get a grasp of „herding“ behavior like the „event-

study“ potentially can when estimating the abnormal changes in volume of stocks and indexes.

Boulton and Bacon (2018) in their work also used “event-study” methodology for

estimation the effect of „BREXIT” vote to ten biggest stock indexes. Boulton and Bacon (2018)

estimated period of 60 days for the “event-study”. Estimation contained 30 days before the event

and 30 after event. Abraham (2018) in his research about New Zealand financial market and

„BREXIT” effect to it estimated 40 days period. He estimated 20 days before the event and 20 days

after it. Ramiah, Pham and Moosa (2017) selected period of 10 days for calculating abnormal

returns. Oehler, Horn and Wendt (2017) in their work modify time period for 5 minutes. This

analyzes of “events-window” selection by different authors suggest that there is no strict rules in

selecting period for calculation period.

31

Boulton and Bacon (2018) in their work calculates the companies reaction using “event-

study” method a.k.a. risk adjusted return model (RAR) in order to calculate how “BREXIT” vote

day affected 10 largest companies traded on NYSE which are strongly related to United Kingdom

market. Authors calculate percentage changes in the stocks prices in that specific period. Risk

adjusted return model calculates the difference between the actual daily prices and expected which

are calculated by adding the β (the slope) with α (intercept) multiplied with daily returns of the

benchmark. Furthermore the abnormal returns are calculated subtracting actual daily returns of the

stock with expected. Furthermore the cumulative abnormal, average and cumulative average

abnormal returns are calculated in the similar way. Concept of the Boulton and Bacon (2018) model

was used in construction of this research model when calculating the effect for the individual

companies listed on London Stock Exchange.

Oehler, Horn and Wendt (2017) use similar approach as Boulton and Bacon (2018) for

calculating “BREXIT” voting period in their work. The difference between their works is that

Oehler, Horn and Wendt (2017) apply more control variables in their work such as industry type the

company is working, its market capitalization and their domestic sales. Oehler, Horn and Wendt

(2017) also use a lot shorter estimation period and only calculates 5 minutes period for detecting

noise trading.

Morales and Andreosso-O‘Callaghan (2019) introduces their econometrical model for

calculation of China‘s financial market index movements influenced by „BREXIT” and Donald

Trump election. Authors applies different control variables such as economical policy uncertainty

(EPU) index and volatility index (VIX) to better grasp the effect of events.

Based on previous authors empirical models and works the empirical model for this

research was constructed. As it was already have been mentioned the estimation period depends on

what research is trying to prove so event period depends on hypothesis and tests. This research

focuses to grasp the effect for short time period before and after the event to evaluate the power of

the event for the market and the expectations and efficiency of the market. “event-window” selected

for the research is 10 days before the event and 10 days after. Event day in the model is considered

0. Daily data series over the period 3rd of May 2016 to 17th of April 2020 were used for the

research.

Equation for calculating percentage change in returns for London Stock exchange is named

below:

𝑅𝐿𝑜𝑛𝑑𝑜𝑛,𝑡 =𝑃𝐿𝑜𝑛𝑑𝑜𝑛,𝑡 − 𝑃𝐿𝑜𝑛𝑑𝑜𝑛,𝑡−1

𝑃𝐿𝑜𝑛𝑑𝑜𝑛,𝑡−1∗ 100 (4)

32

𝑅𝐿𝑜𝑛𝑑𝑜𝑛,𝑡 is return of the London Stock Exchange on the time t, 𝑃𝐿𝑜𝑛𝑑𝑜𝑛,𝑡 is closing price

of the day t, 𝑃𝑠,𝑡−1 is closing price of previous day. The holding period returns of benchmark stock

(S&P 500) was calculated using the equation written below:

𝑅𝑆&𝑃 500,𝑡 =𝑃𝑆&𝑃500,𝑡 − 𝑃𝑆&𝑃500,𝑡−1

𝑃𝑆&𝑃500,𝑡−1∗ 100 (5)

𝑅𝑆&𝑃 500,𝑡 is return of the S&P500 on time t, 𝑃𝑆&𝑃 500,𝑡 is current day closing price of

S&P500 and 𝑃𝑆&𝑃 500,𝑡−1 is closing price of S&P500 on previous day. Using the same formula the

daily returns of the individual companies were calculated:

𝑅𝑠,𝑡 =𝑃𝑠,𝑡 − 𝑃𝑠,𝑡−1

𝑃𝑠,𝑡−1∗ 100 (6)

𝑅𝑠,𝑡 is return of the stocks s on time t, 𝑃𝑠,𝑡 is current day closing price, 𝑃𝑠,𝑡−1 is closing

price of previous day.

This equation is also applied to calculate the daily returns of FTSE 100.

𝑅𝐹𝑇𝑆𝐸 100,𝑡 =𝑃𝐹𝑇𝑆𝐸 100,𝑡 − 𝑃𝐹𝑇𝑆𝐸 100,𝑡−1

𝑃𝐹𝑇𝑆𝐸 100,𝑡−1∗ 100

(7)

A regression analysis was partaken to calculate the α (the intercept) and the β (the slope of

the regression line). This was completed by using the actual daily return of each company

(dependent variable) and the corresponding S&P 500 index (independent variable) over the course

of the pre-event period, -30 days to -10 days.

In this study in order to get normal expected returns, the risk-adjusted return method

(RAR) was used. The expected return for each day of the event period from day -10 to day +10 is

calculated using formula:

𝐸𝑥(𝑅)𝑠,𝑡 = 𝛼 + 𝛽𝑅𝑚,𝑡 (8)

Ex(R) is expected return of the stock and 𝑅𝑚 is return of the market (S&P 500 when

measuring effect for London stock exchange and FTSE 100 when measuring effect for largest

companies in UK). After calculating expected return of the stocks, excess return was calculated

using formula:

𝐴𝑅𝑠,𝑡 = 𝑅𝑠,𝑡 − 𝐸𝑥(𝑅) (9)

Where 𝐴𝑅𝑠,𝑡 is abnormal return for the stock s on time t, 𝑅𝑠,𝑡 is actual return of the stock s

on time t and 𝐸𝑥𝑅 is expected return of the stock. Cumulative excessive returns will be calculated

by adding excess returns of each day from day -10 to day +10.

Cumulative abnormal returns (𝐶𝐴𝑅𝑠,𝑡,𝑖) are calculating using this formula:

𝐶𝐴𝑅𝑠,𝑡,𝑖 = ∑ 𝐴𝑅𝑠,𝑡

𝑖

𝑡

(10)

33

Where 𝐶𝐴𝑅𝑠,𝑡,𝑖 is cumulative abnormal returns of stock s in period form t to i. The “event-

window” in research selected from 10 days before the event and 10 days after. Graph of CAR will

be plotted for the event period i.e. day -10 to +10.

Additionally when calculating the abnormal returns of companies the average abnormal

returns will be calculated in order understand whether the companies outperformed the average

return of sample. Average abnormal returns will be calculated using formula:

𝐴𝐴𝑅𝑡 =𝐶𝐴𝑅𝑡

𝑛

(11)

In this equation 𝐴𝐴𝑅𝑡 is average abnormal returns for all stocks on the given day, 𝐶𝐴𝑅𝑡 is

cumulated abnormal returns of all companies listed in the sample and n is the number of companies

in the sample. After calculating the average abnormal returns for all stock on the given day the

cumulated average abnormal returns will be calculated for the event period. The cumulated average

abnormal returns is calculated by this formula:

𝐶𝐴𝐴𝑅𝑠,𝑡,𝑖 = ∑ 𝐶𝐴𝑅𝑠,𝑡,𝑖

𝑖

𝑡

(12)

Where 𝐶𝐴𝐴𝑅𝑠,𝑡,𝑖 is cumulative average abnormal returns of all stocks s in period form t to

i. The “event-window” in research selected from 10 days before the event and 10 days after.

Additional event periods will be added in the research. Abraham (2018) in his work

estimated periods of not only for 20 days before the event ant 20 days after the event but also

additional ones. Abraham (2018) also calculated periods of 10, 5, 2 days before and after the event

to better understand the significance of the event the closer its happening days. In this paperwork

periods of 5 and 2 days before and after the event will be calculated. These additional two periods

will give a better understanding at which period of time the event has a strongest effect for financial

market and companies. Nevertheless, two additional periods of time will be added in this research.

Periods of 10 days before the event until the event day and then 10 days after the event will be

estimated. Those calculations will be made in order to better understand whether the event meets its

expectations. The period until the event day will show the expectations of the financial market and

events expected effect for it and 10 days after will evaluate the real value of event and show the

efficiency of the market. This model will be applied to calculate the abnormal returns of London

Stock Exchange and individual companies listed London stock exchange. Daily returns of each

individual stock will be calculated as well as for benchmark and etc. All of these calculations and

equations named previously will be used to accept or reject hypothesis H1, H2, H3. As it was

identified previously the H1 will be accepted if at least half of the events will have an effect the

returns of London stock exchange. H2 will be accepted if at least half of the events will have an

34

effect for at least half of the companies in estimating period and lastly the H3 will be accepted if at

least half of the events will have an effect for the financial sector companies compared to others.

In order to accept or reject H4 similar approach was applied. Cumulated Abnormal

Volume were calculated using “event-study” method to detect the herding behavior. Significant

abnormal changes in volume would signalize about existing herding behavior in financial market.

The herding behavior could be negative or positive meaning that some BREXIT news influence

negative herding and others positive.

2.4 Data Sample of the Research

In order to answer whether the „BREXIT“ events have a significant effect for the United

Kingdom financial market 17 different events were selected. Selected dates includes events from

2016 until 2020. These specific events were selected because of their significance towards United

Kingdom relationship with European Union. All of the events with their actual happening day are

named in the table 5 (starts in page 34, ends in 35).

Some of the events listed in Table 5 are discussed in more detail in order to better

understand what happened on that event. The event on 17th of January in 2017 Theresa May in her

first substantial speak announced that she desired to leave EU without staying in the single market.

“Article 50” event finalized the leaving of United Kingdom from European Union. “Divorce bill” is

the sum of money United Kingdom had to pay for European Union to settle the United Kingdom

share of the financing of all the obligations undertaken while it was a member of the European

Union. Lastly “Chequers agreement” stands for the event happened after the Europeans Union

passed “withdrawal bill” that became a law at the end of June. Theresa May took her cabinet back

to country in order to sign off the collective position for the rest of the “BREXIT” negotiations with

the European Union. “BREXIT” secretary David Davis resigned Theresa‘s May new plan.

Table 5

Key dates of the “BREXIT”

Date News

2016-07-16 Theresa May becomes Prime Minister

2017-01-17 „BREXIT“ means „BREXIT“

2017-03-29 May triggered Article 50 of the Lisbon Treaty

2017-06-08 May lost her parliamentary majority

2017-12-08 UK and EU agrees on „divorce bill“

2018-07-06 “Chequers” agreement” is finalized

2018-11-25 At a special meeting of the European Council.

2019-01-15 The First „meaningful vote“ is held on the Withdrawal Agreement in the UK

„House of Commons“

2019-03-12 The Second „meaningful vote” on the Withdrawal Agreement.

2019-04-12 UK’s deadline for leaving the EU was pushed back to 31 October

2019-06-24 Theresa May set a resignation date of 7 June

35

Table 5 continuation

Note: Table has been created by author using data selected from the news portals ‘The week’, ‘BBC news’ and others

Furthermore as it was already mentioned in previous paragraphs 25 different companies

with largest market capitalization listed on London stock exchange is selected for answering the

hypothesis H2 and H3. As it can be seen from the table 6 (starts in page 35, ends in 36) companies

included in to the sample comes from different sectors. Largest number of companies included in

the sample comes from the consumer staples and financial sectors. Sample consists of 7 different

companies for each sector. Furthermore there are 3 companies from industrial sector, 2 from energy

and 2 from healthcare. Last 4 companies from the sample comes from basic materials,

telecommunications, consumer discretionary and utilities sectors.

Table 6

2019-07-24 Boris Johnson wins the Conservative Party leadership race

2019-09-04 After voting to take control of Commons business for the day, members of

parliament backed a bill blocking a 31 October „no-deal BREXIT“.

2019-10-02 Boris Johnson had made a formal proposal to the EU setting out his alternative to

the Irish backstop

2019-10-19 A special Saturday sitting of Parliament was held to debate the revised withdrawal

agreement

2019-12-12 Boris Johnson won general election and broke the Parliamentary deadlock.

2020-01-30 Johnson has passed his withdrawal agreement and paved the way for the UK to

leave the EU

FTSE 100 index largest companies (Market Cap.)

Company Name ICB Industry

Market Cap

(£m)

1 ROYAL DUTCH SHELL PLC Energy 156 941,61

2 HSBC HOLDINGS PLC Financials 112 088,46

3 ASTRAZENECA PLC Health Care 97 413,12

4 BP PLC Energy 92 515,66

5 GLAXOSMITHKLINE PLC Health Care 89 014,40

6 BRITISH AMERICAN TOBACCO PLC Consumer Staples 77 016,86

7 DIAGEO PLC Consumer Staples 70 331,72

8 UNILEVER PLC Consumer Staples 52 962,80

9 RIO TINTO PLC Basic Materials 51 283,21

10 RECKITT BENCKISER GROUP PLC Consumer Staples 44 579,19

11 VODAFONE GROUP PLC Telecommunications 39 969,07

12 LLOYDS BANKING GROUP PLC Financials 39 782,85

13 RELX PLC Consumer Discretionary 38 953,49

14 NATIONAL GRID PLC Utilities 35 286,05

15 PRUDENTIAL PLC Financials 35 167,68

16 BARCLAYS PLC Financials 29 101,06

17 ROYAL BANK OF SCOTLAND

GROUP PLC

Financials 26 401

18 TESCO PLC Consumer Staples 24 180,14

19 EXPERIAN PLC Industrials 23 950,37

20 CRH PLC Industrials 22 510,88

21 ASSOCIATED BRITISH FOODS PLC Consumer Staples 20 789,36

36

Table 6 continuation

Note: The table has been created by author using data from londonstockexchange.com

Companies that are listed in the sample will be named differently when presenting the

results of the research. Companies will be listed not in market capitalization order from largest to

smallest like in the table 6 (starts in page 35, ends in 36) but in the order according to the sector

they produce products or services in. Companies will be coded as numbers in the graphs of the

results. The numeric order of them are presented in the table 7 below.

Table 7

Individual Companies Numeric Code In Graphs Presented in Results

No. Company name

1 ROYAL DUTCH SHELL PLC

2 BP PLC

3 HSBC HOLDINGS PLC

4 LLOYDS BANKING GROUP PLC

5 PRUDENTIAL PLC

6 BARCLAYS PLC

7 ROYAL BANK OF SCOTLAND GROUP PLC

8 STANDARD CHARTERED PLC

9 LEGAL & GENERAL GROUP PLC

10 ASTRAZENECA PLC

11 GLAXOSMITHKLINE PLC

12 BRITISH AMERICAN TOBACCO PLC

13 DIAGEO PLC

14 UNILEVER PLC

15 RECKITT BENCKISER GROUP PLC

16 TESCO PLC

17 ASSOCIATED BRITISH FOODS PLC

18 IMPERIAL BRANDS PLC

19 EXPERIAN PLC

20 CRH PLC

21 BAE SYSTEMS PLC

22 RIO TINTO PLC

23 VODAFONE GROUP PLC

24 RELX PLC

25 NATIONAL GRID PLC

2.5 Limitations of the Empirical Model

Nevertheless this empirical model and research has some limitations. The first limitation is

lack of control variables to understand better the effect of the “BREXIT” events whether they

influence the movement of market prices or control factors which are not included in the model. For

22 BAE SYSTEMS PLC Industrials 20 239,80

23 STANDARD CHARTERED PLC Financials 20 168,10

24 IMPERIAL BRANDS PLC Consumer Staples 18 462,53

25 LEGAL & GENERAL GROUP PLC

Financials 18 224,14

37

example Oehler, Horn and Wendt (2017) in their work used control variables such as domestic

sales, industrial level and market capitalization in to the model because due to the fact that those

besides „BREXIT“ vote could have had influence to the returns of the FTSE 100 index companies.

In this research similar control variables couldn‘t be applied due to the fact that companies selected

for the research were the ones with largest capitalization, with high domestic sales and from just a

few industries so the results would have been biased. Morales and Andreosso-O‘Callaghan (2019)

in their work where they estimate the effect of Donald Trump election and „BREXIT” vote added

economic policy uncertainty in their work and volatility index (VIX). These notes leaves a space for

future researchers to take in consideration and in to their empirical models some of the already

mentioned control variables to grasp a deeper understanding how „BREXIT“ news affect the United

Kingdom financial market.

Secondly the model isn’t capable of removing the duality of the events that happened in

the same period of time. For example there were 3 events that happened in similar periods of time

and their windows intersect. There was one event happened on 28th of August in 2019 another one

on 4tf of September 2019 and third one on 24th of September 2019. Due to this intersection of

“event-windows” two events had to be excluded from the sample. Events that were treated not so

important were excluded from the sample even though they could have had some effect for the

returns of companies from different sectors and London stock exchange.

Third limitation of the research is evaluation of „herding” behavior in United Kingdom

financial market. Due to the limitations of model itself it could be too hard to grasp the „herding“

behavior in the market because it doesn‘t evaluate investors psychological behavior so well as other

models evaluating „herding“ behavior in financial markets. Vidanalage, Shantha (2019) introduces

two existing models for market-wide herd behavior analyzes. The first one was introduced in 1995

by Christie and Huang and also known as cross-sectional standard deviation model (CSSD) and

second one was introduced in 2000 by Chang and his colleagues known as cross-section absolute

deviation model (CSAD). These two models would have been more fitted to understand whether the

“herding” behavior exists in United Kingdom financial market. Due to this fact the results of model

could be insignificant when evaluating „herding” behavior in the United Kingdom financial market.

To summarize the methodology chapter it is possible to say that political uncertainty

influences negative financial markets reaction and results of many previous works proves it right. If

the political uncertainty is not evaluated correctly by the market it could influence the collision of

one. Researchers and many scientists states that today we face the political uncertainty like no

others which is known as „BREXIT“. That is why it is so important to take a deeper look at political

uncertainties like „BREXIT“ and understand how they influence the financial markets of the local

and other countries. Understanding it could help prevent emotional decision making in investing

38

world by the investors, also help to tame the market volatility and understand better what investing

strategy investor should select at that volatile time. As the analyzes of models showed the most

suited empirical model for testing the named three hypothesis is the „event-study” method used by

many authors in their previous researches. „Event-study” method helps trace such “abnormal”

changes, because it follows the general valuation of many investors that (re)examine all the

available data for the estimation of the market value of each traded stock. The research selected 17

different events of „BREXIT“ that happened through 4 year period starting from 2016 and ending

on 2020 to estimate their effect for the London Stock Exchange and individual companies listed on

it. Furthermore to estimate the effect on the company level 25 different companies with highest

market capitalization from different sectors were selected in the research. The companies in the

research comes from financial, industrial, consumer staples, consumer discretions, utilities,

telecommunications, healthcare and energy sectors. Most of the companies selected in the sample

comes from financial and consumer staples sectors. The expected results of the research should

show the negative impact for the returns of selected companies and market itself. Nevertheless the

financial sector should be influenced stronger and more negatively than the others due to the

findings of other researches. Lastly according to previous authors findings „herding” behavior

should be stronger on the days of the event period that is treated as political uncertainty days. That

is why another question for the research is to analyze whether „herding” behavior is present during

„BREXIT” news days. In the next chapter the results of empirical analyzes will be presented.

39

III. EMPIRICAL RESULTS ON “BREXIT” EFFECT FOR

LONDON STOCK EXCHANGE, INDIVIDUAL COMPANIES AND

VARIOUS SECTORS

In this part of the Thesis the results of empirical model will be described and analyzed. To

begin with descriptive statistics of London stock exchange and individual companies will be

presented. Furthermore the calculations of how “BREXIT” news affected London Stock Exchange,

individual companies and various United Kingdom economical sectors will be presented. Later on

the discussion of results compared to previous researches will be analyzed. Lastly the limitations of

the empirical data and recommendations for future researches will be presented.

3.1 Descriptive Statistics of London Stock Exchange, Individual

Companies and Various UK sectors

Descriptive statistics will be presented divided by the companies sector. Descriptive

statistics of financial sector and Consumer staples companies will be separated from the others

sectors due to the fact that both of sectors have 7 companies in the sample. Distribution by sectors

the companies produce products and services will help to understand data more clearly. Estimation

period for the research is selected from 3rd of May 2016 to 17th of April 2020.

Table 8

Descriptive statistics of London Stock exchange index (2016-05-03 to 2020-04-17)

Variable Mean Median Max Min SD Skewness Kurtosis

London

Stock

exchange

Index

0,0009648 0,0004954 0,1427 -0,1016 0,015997 0,39310 12,595

Note: Table was created by author using empirical data of London Stock Exchange collected from investing.com

Descriptive statistics of London stock exchange presented in table 8 above shows that

mean is larger than the median meaning that there are major outliers in the end of the distribution.

That means that London stock exchange returns experienced larger gains due to the some events or

reasons in estimation period. Maximum daily returns for the day was 14.27% percent and largest

loses experienced by London stock exchange were -10.16%. Positive skewness indicates that if the

“random” returns would appear on some daily data for some reasons it probably would be positive

in London stock exchange case. Lastly if the Kurtosis index is above 3 it shows that extreme

changes in daily returns appear more frequently.

40

Table 9

Descriptive statistics of Financial sector companies’ stock prices (2016-05-03 to 2020-04-17)

Variable Mean Median Max Min SD

HSBC Holding -7,1333e-005 0,00015495 0,063104 -0,10005 0,013209

Lloyds Banking

Group

-0,00075817 -0,00086526 0,11569 -0,23579 0,019730

Prudential -0,00024167 0,00077075 0,14365 -0,18255 0,021096

Barclay’s -0,00059557 0,00017177 0,12544 -0,19454 0,021968

Royal Bank of

Scotlands and Group

-0,00073813 -0,00039093 0,12858 -0,19899 0,022517

Standard Chartered -0,00024633 -0,00029395 0,067710 -0,12991 0,017854

Legal and General

Group

-6,5351e-005 0,00075132 0,15406 -0,22643 0,022317

Note: Table was created by author using empirical data of financial sector companies of FTSE 100 selected from

Investing.com

In the table 9 above results of Financial sector descriptive statistics is submitted. First of all

as it can be seen from the table 9 almost every financial sector company except “Standard

Chartered” has a lower mean than median, meaning that extreme changes in daily returns are on

negative side of the population. That means that financial sector companies experienced much more

extreme loses than gains during the research period. Nevertheless largest gain sections compared

with largest loses section shows that loses were much greater than gains for all of the companies in

the table 9. Lastly compared to London stock exchange standard deviation is larger for most

companies except “HSBC Holdings PLC” meaning that financial sector companies are more

volatile than market.

Analyzing descriptive statistics of consumer staples companies in table 10 (page 41) we

can recon some similarities between these two sectors. In most of the cases except for “Diageo

PLC” and “Tesco PLC” mean is lower than the median meaning that more than a half companies

same as the financial sector experienced higher extreme loses during estimation period. Minimum

and Maximum charts ensures that major outliers on low end of sample were a greater than major

outliers in the high end for most of the companies except for “Unilever PLC”. Standard deviation

for most of the companies are greater than the market meaning that they are more volatile compared

with London Stock Exchange index. The only companies in consumer staples sector with lower

standard deviation are “Diageo PLC” and “Unilever PLC”.

41

Table 10

Descriptive statistics of Consumer Staples sector companies prices (2016-05-03 to 2020-04-

17)

Variable Mean Median Max Min SD

British American

Tobacco

-0,00034427 0,00000 0,073238 -0,11227 0,016698

Diageo 0,00037572 0,00019325 0,087220 -0,090933 0,012533

Unilever 0,00028800 0,00039882 0,12600 -0,074282 0,012941

Reckitt Benckiser

Group

-6,7918e-005 0,00000 0,077834 -0,078027 0,013697

Tesco 0,00034063 0,00000 0,093043 -0,094363 0,016533

Associated British

Foods

-0,00044217 0,00000 0,14159 -0,16624 0,017641

Imperial Brands -0,00086978 -0,00046494 0,11590 -0,13859 0,015859

Note: Table was created by author using empirical data of consumer staple sector companies of FTSE 100 selected from

Investing.com

Lastly in the table 11 (starts in page 41, ends in 42) rest of the companies from different

sectors were analyzed. Companies with higher mean than median in the table were “Astrazeneca

PLC”, “Glaxosmithkline PLC”, “Rio Tinto PLC” and “CRH PLC”. That indicates that companies

from “healthcare” sector which were two, didn’t experience more extreme loses than gains.

Nevertheless the minimum and maximum sections show that largest daily loses were higher than

gains for most of the companies except for “BAE systems”. Companies that were less volatile than

the market were “Astrazeneca PLC”, “Glaxosmithkline PLC”, “Relx PLC”, “Vodafone Group

PLC”, “Relx PLC”, “National Grid PLC”, “Experian PLC” and “BAE systems PLC”. Which means

that more than a half of companies in different sectors than financial or consumer staples are not so

volatile compared with the market. Energy sector companies “Royal Dutch Shell PLC” and “BP

PLC” are more volatile in this context and comparison with other companies and market index.

Table 11

Descriptive statistics of sample companies excluding financial and staple sectors prices

(2016-05-03 to 2020-04-17)

Variable Mean Median Max Min SD

Royal Dutch Shell -0,00024702 0,00037927 0,18551 -0,19354 0,018110

BP -0,00019324 0,00010761 0,19544 -0,21671 0,018401

Astrazeneca 0,00071034 0,00053831 0,086414 -0,16737 0,015520

Glaxosmithkline 0,00012066 0,00000 0,069193 -0,080667 0,012472

Rio Tinto 0,00057063 0,0014905 0,13709 -0,12539 0,019574

Vodafone Group -0,00070447 -0,00061877 0,10080 -0,12245 0,015657

42

Table 11 continuation

Relx 0,00041942 0,00070697 0,076821 -0,11430 0,012642

National Grid -9,1823e-005 0,00049817 0,093541 -0,10186 0,013628

Experian 0,00062993 0,00067163 0,11625 -0,11730 0,014548

CRH 0,00012891 0,00000 0,11870 -0,15994 0,017950

BAE Systems 0,00015892 0,00033007 0,10008 -0,081762 0,014481

Note: Table was created by author using empirical data of companies of FTSE 100 selected from Investing.com

To summarize analyzes of descriptive statistics it can be said that in estimation period 13

companies were more volatile than market and 12 weren’t. Largest portion of companies that are

more volatile than the market and more sensitively reacts to changes in it were from largest sample

sectors: financial and consumer staples. 6 companies out of 7 were more volatile than the market in

financial sector and 3 out of 7 were more volatile than the market from consumer staple sector.

Secondly results showed in most of the company cases their mean were lower than the median

meaning that companies experienced large loses more times than they experienced large gains in

estimation period due to some events or reasons. That indicates that market reacted negatively to

those news, events or announcements. After analyzes of descriptive statistics and understanding the

results it shows in the next paragraphs the calculations of empirical model will be presented. The

results will be presented in tables, graphs and etc. that will help to accept or reject the hypothesis.

3.2 “BREXIT” News Effect for London Stock Exchange

First of all the results of “BREXIT” events effected United Kingdom market will be

presented. As it was already mentioned the numbers of events taken for estimation were 17.

Estimation periods selected for research as it was already mentioned were 10 days before and after

event, following with 5 days before and after event, 2 days before and after event, 10 days before

event with event day and lastly 10 days after the event.

Results are presented in the Annex 2. All of the periods of events that had a significant

effect were marked in the Annex 2 table. For all of the “event-windows” separate column bar charts

had been drawn to better understand witch and how many of events had a significant effect for

United Kingdom financial market.

43

Figure 1 shows the percentage changes of United Kingdom markets returns in event

period during all 17 events in 10 days before and after the event period. The events that are

statistically significant are marked with a star simbol above column in the graph. 5 events out of 17

were statistically significant in the 10 days before and after period. As the figure 1 shows 3 out of

the 5 statistically significant events had positive influence for the market returns and in 21 days

period market index showed significant growth of returns. Strongest grow were calculated on the

12th event which was the election of Boris Johnson as new Prime Minister of UK. The returns of the

market have grown more than 18% on that “event-window”. Furthermore the 16th event when Boris

Johsnon won general election and broke the parliamentary deadlcok of “BREXIT” which slowed

down “BREXIT” implementation. The third event that influenced the growth of London Stock

exchange index was Theresa’s May lost in parliamentary majority and chance to increase her

authority in the parliament. Both of the events increased the returns of the market more than 7%.

That suggests that H1 should be rejected due to the fact that only 29% of “BREXIT” events had a

significant influence for the financial market. But due to the reason that maybe events had a shorter

significance period shorter periods need to be analyzed and calculated.

*

*

*

*

*

-10,50%

-9,00%

-7,50%

-6,00%

-4,50%

-3,00%

-1,50%

0,00%

1,50%

3,00%

4,50%

6,00%

7,50%

9,00%

10,50%

12,00%

13,50%

15,00%

16,50%

18,00%

19,50%

21,00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Per

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"BREXIT" events

Figure 1. London Stock Exchange Cumulated Abnormal Returns CAR (-10:+10)

44

Figure 2 above shows how all “BREXIT” events had effected the returns of London Stock

Exchange in other selected event periods. The graph shows all other four selected events periods

and their statistical significance for the returns of London Stock exchange. The results reveals more

detailed information about the significance of the events to UK financial market than in the figure 2.

5 events that were statistically significant in period of 10 days before and after event showed

significance in shorter “event-windows” as well. That is why these 5 events were excluded from

analyzes of shorter periods. What can be said from this data that 3 events showed significant

influence to London Stock Exchange on period of 2 days before and after the event. Theresa’s May

election period, Article 50 and parliament block of 31 October „no-deal Brexit“ had statistically

significant influence for London Stock exchange in that period. Daily returns in that period were

positive in all of the events. 8 event showed significant changes for market returns on period of 10

days before event. On that event period first meaningfull vote was held where UK government were

defeated. Lastly the the speech of Theresa May regarding “BREXIT” and her statements about

future of relations between UK and EU held on 2017-01-17 which is coded as number 2 in this

graph showed significant positive effect for the market in the period of 10 days after the event.

These results show that additional periods for the events show that they were significant in shorter

periods of time. Nevertheles the results of figure 2 do not help to approve of H1.

To summarize the results from these two tables it could be said that “BREXIT” news have

a significant influence for United Kingdom financial market. The difference is that some of the

events have a significant effect in shorter period of time. For example only 5 events out of 17 were

significant in 10 days before and after the event and aditional calculation of other periods increased

* *

*

*

**

*

* *

*

**

*

**

*

*

*

-10,00%

-5,00%

0,00%

5,00%

10,00%

15,00%

20,00%

25,00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17

Per

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"BREXIT" events

CAR (-5;+5) CAR (-2;+2) CAR (-10;0) CAR (0;+10)

Figure 2. Cumulated Abnormal returns (CAR) of London Stock Exchange

45

the sample of significant events for the financial market. The number of significant events increased

from 5 to 10 events out of total 17. 3 events showed significant positive results for daily returns of

London Stock Exchange in 2 days before and after period. 1 event showed significant results on 10

days before the event period and 1 in 10 days after the event. Furthermore the results in 7 cases out

of 10 were positive for the market and increased in the value of daily returns. Nevertheles

distribution of significant periods of events can’t let accept the fact that “BREXIT” events had a

significant effect on UK financial market.

3.3 “BREXIT” News Effect for Individual Companies

Furthermore the effect of “BREXIT” news for the individual companies with largest

market capitalization will be analyzed and presented. All of the calculations for all “event-

windows” were made from annexes starting from 3 to 7. From the tables of annexes from 3 to 7 the

cumulated abnormal average returns of all companies on specific event were calculated and written

in the second row of the table. After that all of the individual companies returns in “event-window”

were compared with that index. At first the “event-window” of 10 days before and after the event

will be analyzed. Results are presented in the figure 3 below. The red arrow in the graph shows the

cumulated average abnormal returns of all companies for all events. In the event period of 10 days

before and after the event it is 0.14%. Companies are coded as numbers from 1 to 25 in the graphs

and their numeric name were presented in methodology part in the table 7. Furthermore statistically

significant changes in daily returns for individual companies regarding “BREXIT” events were

marked with data labels and star symbol meaning that changes in returns were significant in

selected “event-window”. All return data of individual companies presented in the figures

represents the average change in returns during all events for “event-window”.

Figure 3 (page 46) shows that statistically significant changes in average cumulated

abnormal returns for all 17 events were experienced by 9 companies out of 25 in “event-window”

period of 10 days before and after the event. Energy companies “Royal Dutch Shell PLC” and “BP

PLC” experienced significant changes in returns due to the “BREXIT” events and on average their

returns in that period decreased by -2.25% for “Royal Dutch Shell PLC” and -1.83% for “BP PLC”.

4 financial sector companies out of 7 aswell experienced significant changes in returns regarding

“BREXIT” events. “Barclay’s PLC”, “Standard Chartered PLC” and “Legal and General Group

PLC” experienced gains and only “Prudential PLC’ experienced loses due to the “BREXIT” events

when taking average of all returns. On average “Barlcay’s PLC” have grown by 3.21%, “Standard

Chartered PLC” experienced growth by 2.63% and “Legal and General PLC” had largest gains

compared to others by 4.47%. Nevertheless “Prudential PLC” experienced losses of -1.83% on

average regarding “BREXIT” events. “Associated British Foods PLC” experienced losses of -

1.88% on average regarding “BREXIT” events. Other two companies “BAE PLC” and “National

46

Grid PLC” experienced gains on average cumulated abnormal returns on estimation period of 10

days before and after the event. “BAE PLC” experienced gains of 1.91% and “National Grid PLC”

returns had grown by 1.49%. This estimation period suggests that “BREXIT” events didn’t have

signficant effect for larger part of individual companies listed on London Stock exchange and H2

should be rejected. Which means that only a few companies of this sample experienced the effect of

“BREXIT” events on period of 21 days. Due to this reason shorter periods of time are calculated

and analyzed.

Figure 4 (page 47) represents cumulated average abnormal returns of individual companies

for all 17 “BREXIT” events on period of 5 days before and after the event. Data labels and star

simbol represents that returns were statistically significant compared to average market returns

during that period of time. The results in figure 4 shows that “BREXIT” events affected more

companies on shorter period of time then on previously analyzed longer period. 12 companies out

of 25 experienced significant changes in returns regarding “BREXIT’ news. The number of

financial sector companies who experienced significant changes in returns increased from 4 to 5.

“Barclay’s PLC”, “Royal Bank of Scotland and Group PLC”, “Standard Chartered PLC”, “Legal

and General PLC” and “HSBC Holdings PLC” experienced significant gains in returns in that

period compared to average market returns of that event period. “HSBC Holdings PLC”

experienced gains of 0.86% on average returns due to the “BREXIT” events. “Barclay’s PLC”

returns increased by 1.95%,“Royal Bank of Scotland and Group PLC” by 1.78% , “Standard

Chartered PLC” by 1.8% and largest gains of returns were calculated for “Legal and General PLC”.

“Legal and General PLC” returns increased by 3.54% on average due to the “BREXIT” events.

Telecomunications company “Vodafone Group PLC” as well as financial sector companies

experienced gains by 1.6% on average in estimated period of time. Consumer staples company

-2,25%*-1,83%* -1,83%

3,21%*

2,63%*

4,47%*

-1,88%*

1,91%*1,49%*

-3,00%

-2,00%

-1,00%

0,00%

1,00%

2,00%

3,00%

4,00%

5,00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25Per

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Companies

Figure 3. Cumulated Average Abnormal Returns of Sample Companies (T= -10:10)

47

“British American Tobacco PLC” average returns incresed by 1.4% on estimation period regarding

“BREXIT” news. All other companies experienced loses in returns regarding “BREXIT” news in

estimation period. “Royal Dutch PLC”, “Glaxosmithkline PLC”, “Unilever PLC”, “Associated

British Foods PLC” and “Rio Tinto PLC” experienced loses. Largest loses were calculated for

“Associated British Foods PLC” and it returns decreased by -1.81%. After it folows “Rio Tinto

PLC’ with loses of -1.34%, “Glaxosmithkline PLC” with loses of -1.27% and “Unilever PLC” with

loses of -1.19% on average due to the “BREXIT” news. Lowest loses were experienced by “Royal

Dutch Shell PLC” and it was -0.87% on average in estimation period. Nevertheles the results of

“event-window” states that H2 should be rejected because the effect of “BREXIT” events were

insignificant for larger number of companies than it were significant.

Figure 5 (page 48) represents the calculations of “event-window” for 2 days before and

after the event. The results shows that period of 2 days before and after the event have even stronger

significance to daily returns of individual companies listed on United Kingdom financial market. In

this event period 13 companies experienced significant changes in daily returns due to the

“BREXIT” events. 6 out 7 companies from financial sector experienced significant changes in daily

returns due to the “BREXIT” events. What is more important in this specific period all of the

financial sector companies experienced gains in daily returns. Largest gains were calculated for

“Legal and General PLC”. Their returns increased by 2.48% on average in this period. Furthermore

“Royal Bank of Scotland and Group PLC” experienced gains in returns by 1.76%. Other 3 financial

sector companies experienced similar gains in returns. “Standard Chartered PLC” returns increased

by 1.36%, “Barclay’s PLC” returns increased by 1.34% and “Prudential PLC” experienced gains of

-0,87%*

0,86%*

1,95%*

1,78%*

1,80%*

3,54%*

-1,27%*

1,40%*

-1,19%*

-1,81%*

-1,34%*

1,60%*

-3,00%

-2,00%

-1,00%

0,00%

1,00%

2,00%

3,00%

4,00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Per

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Companies

Figure 4. Cumulated Average Abnormal Returns of Sample Companies (T= -5:5)

48

1.29% on average. Lowest growth in returns were calculated for “HSBC Holdings PLC”. Their

returns increased by 0.64% on average. All of the other companies from different sectors

experienced loses in daily returns in this “event-window”. “Royal Dutch Shell” experienced loses of

-0.85% on average for daily returns in this period, “Astrazeneca” experienced loses of -0.61% on

average. 3 out of 7 consumer staples sector companies experienced significant loses in daily returns

in this period and largest loses was experienced by “Reckitt Benckiser Group PLC”. Their loses on

average were -1.14%. After it folows “Diageo PLC” with loses of -0.72% and “Unilever PLC” with

loses of -0.64%. Basic resources company “Rio Tinto PLC” experienced loses on average returns in

this period. Their loses on average were -0.84%. Lastly “Relx PLC” experienced loses of -0.59%

due to the “BREXIT” events. Results of this “event-window” suggests that H2 should be accepted

because the effect of “BREXIT” events were significant for a larger number of companies than it

were insignificant and tha larger number of companies experienced loses due to “BREXIT” events.

Figure 6 (page 49) shows the fourth estimated “event-window” results. It represents the

cumulative average abnormal returns of companies in period of 10 days before the event until the

event day. The significant changes in daily returns were calculated for 15 companies out of 25. It is

the strongest effect for returns of companies calculated so far. The results of this period is similar to

already analyzed 2 days before and after the event period. Most of the financial sector companies

experienced the gains in this period except for “Prudential PLC”. “Prudential PLC” in this period

experienced significant losses of -1.18%. As in the previous estimated “event-window” the largest

gains were experienced by “Legal and General PLC”. Their returns increased by 2.68% in

estimation period on average. Other companies returns do not differ so much from the “Legal and

-0,85%*

0,64%*

1,29%*

1,34%*

1,76%*

1,36%*

2,48%*

-0,61%*

-0,72%*

-0,64%*

-1,14%*

-0,84%*

-0,59%*

-1,50%

-1,00%

-0,50%

0,00%

0,50%

1,00%

1,50%

2,00%

2,50%

3,00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Per

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Figure 5. Cumulated Average Abnormal Returns of Sample Companies (T= -2:2)

49

General PLC”. The second largest gains were experienced by “Standard Chartered PLC”. Their

returns increased by 2.65%. After it folows “Barclay’s PLC” with gains of 2.38% on average in

estimation period. “Royal Bank of Scotland and Group PLC” returns increased by 1.81% and

“Lloyds Banking Group PLC” by 1.7%. “BAE Systems PLC” as well experienced gains in returns

during the estimation period. Their returns increased by 1.7%. All other companies that have had

significant changes in their returns due to the “BREXIT” events experienced negative changes.

Consumer staples sector, Energy sector, Health-care sector, industrial sector and media sector

companies experienced loses in their returns in estimation period. Largest loses were experienced

by “British American Tobacco PLC”. Their average returns decreased by -2.58%. Second largest

loses were experienced by “Astrazeneca PLC”. Their average returns decreased by -1.71%. After it

folows “Associated British Foods PLC” with loses of -1.47%. These three companies experienced

largest loses during this estimation period regarding “BREXIT” events. “Imperial Brands PLC”

experienced loses of -1.13%, “Relx PLC” -0.97% and “Diageo PLC” -0.98%. Energy companies

“Royal Dutch Shell PLC” and “BP PLC” experienced losses of -0.69% for “Royal Dutch Shell

PLC” and -0.83% for “BP PLC”. The results of this “event-window” determines that H2 should be

accepted due to the reason that “BREXIT” events had a significant effect for a larger number of

sample companies and the larger number of companies experienced loses in that “event-window”.

Lastly the “event-window” period of 10 days after the event will be analyzed. The results

are presented in the Figure 7 (page 50). As we can see from the Figure 7 significant changes in

returns were only calculated for 4 companies out of 25. Which means that effect of “BREXIT”

events to the companies met the expectations of the market for most of them before the event day. 2

-0,69%*

-0,83%*

1,70%*

-1,18%*

2,38%*

1,81%*

2,65%* 2,68%*

-1,71%*

-2,58%*

-0,98%*

-1,47%*

-1,13%*

1,70%*

-0,97%*

-3,00%

-2,00%

-1,00%

0,00%

1,00%

2,00%

3,00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Per

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Companies

Figure 6. Cumulated Average Abnormal Returns of Sample Companies (T= -10:0)

50

out of 4 companies experienced significant gains in this period. One of them were “Legal and

General Group PLC” and another one were “British American Tobacco”. “Legal and General

Group PLC” returns increased by 1.8% and 1.52% for “British American Tobacco”. “Royal Dutch

Shell PLC” experienced loses of -1.55% on average in this event period. Lastly “Tesco”

experienced loses of -1.33% during estimation period. The results of this “event-window”

determines that H2 is rejected.

To summarize the results of figures it is valid to say that “BREXIT” events and news have

a significant effect for the individual companies listed on United Kingdom financial market. The

strongest effect it has on the periods of 2 days before and after the event and 10 days until the event.

Nevertheles deeper analyzes of results in those two periods showed that “BREXIT” news have a

negative effect for a larger number of companies and their returns. In 2 days before and after the

event period 7 companies experienced loses in their returns and 6 experienced gains. On the period

of 10 days until the event 9 companies experienced decrease in returns regarding “BREXIT” news.

Other 6 whose changes in returns were significant experienced gains. That suggests that companies

do experience negative impact because of the “BREXIT” news. Longer period of 10 days before

and after the event only showed significance in return changes only for 9 companies. The next

period of 5 days before and after the event increased the significant results sample by additional 3

companies resulting in total of 12 but still to accept the significance of the “BREXIT” events for

companies was not enough. Lastly the last period of 10 days after the event showed that changes in

returns were insignificant in most of the cases. Only 4 companies experienced significant changes

-1,55%*

1,80%*

1,52%*

-1,33%*

-2,00%

-1,50%

-1,00%

-0,50%

0,00%

0,50%

1,00%

1,50%

2,00%

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25

Per

cen

tag

e C

ha

ng

e

Companies

Figure 7. Cumulated Average Abnormal Returns of Sample Companies (T= 0:+10)

51

in returns due to the “BREXIT” events. Which means that expectations of the market were met

before the day of the event.

3.4 “BREXIT” News Effect for Various United Kingdom Economical

Sectors

After analyzing the effect of “BREXIT” news to individual companies with largest market

capitalization listed on United Kingdom financial Market the analyzes of sectoral analyzes will be

presented. The Hypothesis formulated before stated that financial sector companies due to the

“BREXIT” announcement experienced larger loses than other sectors. That is why it is necessary to

evaluate how individual sectors were affected by the “BREXIT” news and whether the financial

sector still suffered more than the others. To analyze that companies from the sample were

structured by sectors and their average abnormal returns were added together. After that statistical

significance of the changes betweens sectors were calculated.

Results presented in figure 8 (page 52) shows that financial sector companies experienced

largest changes in returns compared to other sectors. Actual changes in daily returns of various

sectors are presented in the annex 8. Statistical analyzes contemplates that financial sector

companies experienced larger changes compared to other sectors. On 10 days before and after the

event “event-window” financial sector experienced 11.04% growth in average returns. Other sectors

didn’t experience such a large increase in returns. Industrial sector experienced increase in average

returns by 1.1%, Telecommunications increased their returns by 1.9% and utilities sector returns

have grown by 1.49%. Other sectors experienced losses in returns during 10 days before and after

the event period. Largest loses were calculated in Consumer staples sector and energy sector.

Consumer staples sector returns decreased by -4.39% and energy sectors returns decreased by -

4.07%. Shorter “event-windows” complements first findings about sectors and return changes in

them. Different results only can be seen in estimation window of 10 days after the event. Financial

sector companies experienced 0.34% growth on average returns while other sectors experienced

similar changes in returns in the same period. Only energy sector companies experienced larger

loses compared to other sectors. Energy sectors average returns decreased by -2.55% in that “event-

window”. Nevertheles statistical analyzes confirms that financial sector companies are more

sensitive to “BREXIT” news and experience greater changes in returns because of it. But the

results of this research differs from other authors findings because they find out that “BREXIT” had

a negative effect for financial sector companies while this research suggests differentely. Findings

of this research states that “BREXIT” news has a positive outcome for returns of financial sector

companies. Due to this research results H3 is rejected because “BREXIT” news affected daily

returns of financial sector companies positively and it differs from hypothesis and other authors

findings.

52

Lastly as it was mentioned before the “herding” behavior are more present during times of

uncertainty. That is why this research also tried to estimate whether the presence of “herding”

behavior existed in United Kingdom fnancial market during “BREXIT” events. For estimation of

“herding” behavior same model were applied as for estimation of the abnormal returns.

Nevertheless the results of the model didn’t show any significant “herding” behavior during

“BREXIT” events. This could have happened because of the limitations of the model itself.

Furthermore the comparison of this research to other authors works about “Brexit” effect

for financial markets and individual companies will be analyzed and presented. Bashir et al., (2019)

analyzed the effect of “BREXIT” pre and post refferendum phase for financial markets. They tested

how “BREXIT” refferendum affect UK financial market and 4 other European countries

considering named periods of time. They applied detrend fluctuation analyzes in order to analyze

how “BREXIT” affected the UK, France, Germany, Spain and Netherlands financial markets. The

results of their research indentify negative correlation between UK, France, Germany and

Netherlands financial markets meaning that in the pre-refferendum phase UK experienced

depreciation of currency and increase afterwards. According to Bashir et al. (2019) it is due to the

fact that international investors took a more forward approach and investing in stock markets in

order to benefit from the volatile depreciated currencies after the referendum. Guedes, Ferreira,

Dionisio and Zebende (2019) did a similar research to Bashir et al., (2019). Guedes, Ferreira,

Dionisio and Zebende (2019) research applied more EU countries in sample than Bashir et al.,

11,04%*

9,75%*

9,11%*

10,71%*

-6,00%

-4,00%

-2,00%

0,00%

2,00%

4,00%

6,00%

8,00%

10,00%

12,00%

PE

rcen

tag

e C

ha

ng

e

Sectors

CAAR (-10:10) CAAR (-5:5) CAAR (-2:2) CAAR (-10:0) CAAR (0:10)

Figure 8. Cumulated Average Abnormal Returns of Distinct Sectors

53

(2019) but they also used the detrend fluctuation analysis in their work. Their results determined

that “BREXIT” referendum affected negatively the relations between EU countries meaning

decreases in returns of the market. The coefficient showing the decrease in relations between UK

and other EU countries didn’t decrease only between UK and Bulgaria, Malta and Slovenia.

Aristeidis and Elias (2018) estimated the effect of referendum for London stock exchange. Authors

determined that London Stock exchange experienced loses of 3% on total in the first day of

announcement meanining that “BREXIT” had a negative impact for the UK financial market. Their

findings also supplemented the findings of Bashir et al., (2019) showing the devalvation of pound

sterling due to the “BREXIT” referendum. Aristeidis and Elias (2018) estimated the largest sample

of countries including not only European Union countries but also from North and South America,

Asia and Africa. Abraham (2018) in his research about “BREXIT” effect to New Zealands financial

markets determined that “BREXIT” negatively influenced New Zealand financial market in pre-

referendum phase and it only recovered a few weeks after it. The results of Abraham (2018)

compared to Bashir et al., (2019) show similar trends in the markets because New Zealand is close

trading partner with UK and turmoil before the vote of “BREXIT” in UK affected New Zealand

while helped to recover after it sooner because international investors invest in New Zealand

companies located in UK for the same reason they invested to UK stocks because of the decreased

value of them due to the “BREXIT” vote. Morales and Andreosso-O’Callaghan (2019) also

analyzed how “BREXIT” news affected the Chinese stock market. Authors used “event-study”

method for their research and their findings determined that Chinese stock market didn’t suffer from

“BREXIT” announcement. Their findings approve of hypothesist that countries with weaker trading

relations are not affected as much as countries with stronger relations. The findings of this research

differs from the results in this research.

3.5 Discussion of Research Results

This research demostrates different results compared to all other authors works. In this

research UK financial market experienced insignificant effect regarding the “BREXIT” news. The

results showed that only 5 events out of 17 were significant in the longest event period of 10 days

before and after the event. Furthermore the estimation of shorter event periods only increased the

significance value of additional 5 events on different periods of “event-window”. That is why it is

imposible to state which event period is significant for UK financial market in the analyzes and to

determine whether the “BREXIT” news and events have a significant effect for UK financial

market. What is more important is that results of the research differ a little bit from the previous

authors work. Bashir et al., (2019), Guedes, Ferreira, Dionisio and Zebende (2019), Aristeidis and

Elias (2018) and Abraham (2018) asses that “BREXIT” news had a negative influence to the

relationships and returns of UK and other countries financial markets in pre-referendum phase and

54

only recovering after it. While this research findings suggest that this is not always a true. In some

cases returns of the market are also positive in the pre-event period. 6 events showed increase in

returns of the market before the event meaning that not always the results should be negative until

the event. Theresa May election, Article 50, 1 and 2 meaningful votes, Block of October leaving

and UK leaving day of EU were the events that influenced positive returns to UK financial market

until the event. Nevertheless in most cases the growth in returns can be seen in post event period

suggesting the recovery of market regarding political instability. Aristeidis and Elias (2018) identify

that abnormal returns and volatility are more present in closer dates to event while this research

results didn’t identify the same. Only 6 out of 17 events were significant in short event period.

Furthermore from the company point of view results show some interesting insights. The

research were focused on companies with largest market capitalization and “BREXIT” news and

events effect for them. Results of the research shows that statistically significant “event-windows”

in the research were 2 days before and after the event and 10 days until the event. Both of the

estimation periods show that more companies suffered decrease in returns due to the “BREXIT”

events. In 2 days before and after the event period 6 companies experienced growth of returns while

7 experienced loses and on the other “event-window” 9 companies experienced significant loses in

their returns while only 6 of them experienced growth of returns. Other estimated periods and their

statistical significance were to low to consider it meaningful. “Event-window” of 10 days before

and after the event showed that only 9 companies out of 25 experienced significant changes in their

returns meaning that “BREXIT” events do not have significant impact on longer period of time.

Shorter period of 5 days before and after the event increased the sample of significant changes in

returns to companies but still were to low to consider it meaningful. Lastly the “event-window” of

10 days after the event showed the smallest quantity of significant changes in returns for individual

companies. Only 4 companies out of 25 experienced significant changes in returns due to the

“BREXIT” events. Results suggest that investors evaluate the effect of the event before the event

date and for most companies those evaluations were right. Compared to Boulton and Bacon (2018)

findings this research suplements their findings about “BREXIT” effect to individual companies.

Boulton and Bacon (2018) determined that “BREXIT” referendum had a negative effect for largest

companies listed on New York Stock Exchange which were closely related to UK market.

Furthermore Ramiah, Pham and Moosa (2017) in their research calculated that most of the sectors

experienced negative returns due to the “BREXIT” events. This research suplements their findings

as well showing that for most individual companies from different sectors “BREXIT” news had a

negative impact. Shahzad, Rubbaniy, Lensvelt and Bhatti, (2019) results sumplement strongly with

findings of this research. Authors estimated not only the effect for the whole market but also on

company level. Their results showed that individual companies returns decreased significantly in

55

pre-referendum phase and experienced growth only after political stabilization and markets

evaluation of real situation. This research findings supplements their finding showing that most of

the companies suffered losses in the “event-window” of 10 days until the event. Abraham (2018) in

his work measured that New Zealand companies experienced loses because of UK wish to leave

EU. Abraham (2018) aswell as Shahzad, Rubbaniy, Lensvelt and Bhatti, (2019) determined that

strongest negative effect for individual companies were observed on period of time until the

“BREXIT” vote. Furthermore Andrikopoulos, Dassiou and Zheng (2020) in their work analyzed the

effect of “BREXIT” referendum to FTSE 100, IBEX35 and DAX30 non-financial companies. Their

findings identified that FTSE100 25 non-financial companies from different sectors experienced

growth in returns and that referendum were positive for them. Andrikopoulos, Dassiou and Zheng

(2020) results compared to this research findings differs because as it shows individual companies

from non-financial sector experienced significant negative returns due to the “BREXIT” while in

most cases financial sector companies experienced growth in returns. Same interpretation could be

implemented about the other significant results showing “event-window”. Period of 2 days before

and after the event showed that larger number of companies suffered decrease in returns due to the

“BREXIT” events.

To summarize the results of the “BREXIT” effect for individual companies it is possible to

say that this research results suplements other authors findings about negative “BREXIT” effect to

individual companies. In “event-windows” that indicate significant changes for returns of individual

companies larger number of companies suffered loses due to the “BREXIT” events. In “event-

window” of 10 days before the event 9 companies suffered loses while only 6 experienced growth

in returns. On the shorter significant period of 2 days before and after the event 6 companies

experienced growth in returns while 7 had loses.

Ramiah, Pham and Moosa (2017) in their work analyzed how “BREXIT” affected various

sectors of UK economy. They analyzed how the referendum affected them and which sectors

experienced loses and which gained from it. Their findings indentified that financial, travel and

leisure sectors suffered largest loses due to the referendum. Their results determined that chemicals,

oil and gas, beverage, aerospace and defence, tobacco and forestry and papers sectors experienced

gains due to the “BREXIT” referendum. Meaning that only 6 sectors out of 24 gained positive

returns due to it. This research results suplements findings of Ramiah, Pham and Moosa (2017)

work in a certain way that larger portion of sectors included in the sample experienced loses. In this

research 5 out of 9 sectors experienced loses aswell. Nevertheless the main question of this research

whether the “BREXIT” news affect negatively and significantly stronger financial sectors compared

to others. As the results in Figure 8 shows financial sector companies indeed had a stronger effect to

their daily returns due to the “BREXIT” events. The difference between this research findings

56

compared to Ramiah, Pham and Moosa (2017) is that financial sector companies experienced gains

due to the “BREXIT” events compared to other sectors. Financial sector companies returns

increased by 11.04% on average due to the “BREXIT” events in period of 10 days before and after

the event. Compared to sector in second place with positive returns, financial sector returns were

larger more than 5 times. Ramiah, Pham and Moosa (2017) in their work calculated loses in

financial sector which were -15.37%. That means that this research findings completelly differs

from Ramiah, Pham and Moosa (2017). Aristeidis and Elias (2019) in their work about how

“BREXIT” referendum affected various countries financial markets also separated banking sector

results from others. They focused on findings about financial sector of United Kingdom. In their

research they estimated that banking sector experienced huge loses due to the uncertainty created by

the “BREXIT” referendum. Their results showed that 5 largest banks of UK suffered decrease in

their stock prices by 21% on average on the next morning after referendum. Additionally a non-UK

banks stocks prices also fell by more than 10% on average. Not all banks fully recovered after it and

some of them prices still remain lower than 10%. Bank of England released 150£ to banks in

lending by reducing the countercyclical capital buffers that banks are required to hold. Nevertheless

the results of Aristeidis and Elias (2019) work shows that financial sector suffered from due to the

“BREXIT” referendum vote and had to take some time to recover. Aristeidis and Elias (2019)

results differ from the findings of this research results.

Table 12

Hypothesis valuation

Hypothesis

No.

Hypothesis Accepted/Rejected

H1 “BREXIT” news have a significant negative effect on

London stock exchange.

Rejected

H2

“BREXIT” news have a significant negative effect on

individual companies in United Kingdom listed on London

Stock Exchange.

Accepted

H3 “BREXIT” news have a stronger negative effect to financial

sector companies compared to other sector companies.

Rejected

H4 “BREXIT” news trigger financial herding behavior in

London stock exchange.

Rejected

Results of the acceptance and rejection of research hypothesis are presented in th table 12.

First hypothesis of the research is rejected. H1 is rejected due to the reason that only 5 events in the

research were statistically significant to London Stock Exchange in 10 days before and after the

event period. Calculations of shorter “event-windows” didn’t increase the statistical significance of

the “BREXIT” events to London Stock exchange. Of course some of the events showed an increase

in their significance in shorter event period but still the number of significant events didn’t increase

to a number that could let accept the H1. Another reason why the H1 can’t be accepted is that larger

57

portion of events influenced the growth of the market. 12 out of 17 events influence the growth of

London Stock exchange returns.

H2 is accepted because as the results of the research shows larger number of companies in

the sample experienced loses in their returns due to the “BREXIT’ events. 7 companies out of 13 in

the “event-window” of 2 days before and after the event experienced significant loses and 9

companies out of 15 had loses in “event-window” of 10 days before the event. Other “event-

windows” were too insignificant to identify whether the effect for individual companies were

negative or positive due to the “BREXIT” events.

H3 were based on previous researchers works and stated that financial sector companies

experienced larger loses due to “BREXIT”. Aristeidis and Elias (2019) and Ramiah, Pham and

Moosa (2017) in their works stated that financial sector companies experienced large loses due to

“BREXIT” referendum. This research approves with the point that financial sector companies were

more sensitive to “BREXIT” referendum and their prices were more volatile. However research

results determined other results when analyzing the actual returns of sector. Research results shows

that financial sector companies increased in value and their returns have grown because of

“BREXIT” events and dissaproves with previous authors results. Because of this reason this

hypothesis is rejected.

H4 is rejected aswell because model couldn’t detect any financial herding behavior in the

“event-windows” of “BREXIT” events. Changes in volume for all individual companies and

London Stock Exchange were statistically insignificant. That is why it can’t be said whether the

financial “herding” behavior were present in the UK financial market or not.

3.6 Limitations of the Empirical Data Findings and Reccomendations

for Future Researches

Lastly empirical research has some limatations and they reflect in the work. First limitation

of research is lack of control variables in the work that could intersect with “BREXIT” news. As the

results showed “BREXIT” events had a significant effect for the companies and their returns in

“event-windows”. But this research do not take in to account the influence of different factors like

economic policy index and volatility index VIX that were applied by Morales and Andreosso-

O‘Callaghan (2019) in their research. Those two indexes more or less would have influenced the

final results of the research at least for financial market index of UK. As it was already mentioned it

would be hard to select control variables for individual companies due to the reason that companies

in the research selected were largest based on market capitalization had high domestic sales and just

from a few industries. Nevertheless if future researches would focus on results for larger sample of

companies and “BREXIT” news affect for them those control variables should be applied.

58

Second limitation of the research is duality of the events. As it was already have been

mentioned some of the “BREXIT” events and news intersected and due to this reason some of them

couldn’t be estimated and had to be excluded from the research. This logic applies to all news that

could have had influenced UK financial market and individual companies. Some of the events like

dividend announcement, earning reports and etc. could have had an influence to decrease or growth

of returns for individual companies. Other political news as well could have had some influence for

UK financial market and those companies. Future researches should take in to consideration and

analyze every other events, news happening around the “BREXIT” events.

Last limitation as it was already mentioned is that model couldn’t able to detect any

presence of financial “herding” behavior in UK financial market. Political uncertainty effects the

financial markets of countries negatively and increases financial “herding” behavior and that was

analyzed and approved by previous researches. “BREXIT” is new political uncertainty and should

increase financial “herding” behavior as well as others because UK exit would decrease the power

of EU significantly. However the results of the model didn’t show any signs of financial “herding”

behavior in UK financial markets and individual companies meaning that financial “herding”

behavior is not present in the “BREXIT” event periods. Future researches should apply different

and more frequently used empirical models for better detection of financial “herding” behavior in

order to answer whether “BREXIT” affected it.

59

CONCLUSIONS

1. Traditional finance theory is finance theory that defines investor as rational, risk-averse and

aimed to maximize “expected utility” at every decision he makes. Behavioral finance theory is

finance theory that considers investor not as rational but as some authors describe it as

“normal”. Behavioral finance theory states that investors can make mistakes in their calculations

(cognitive errors). Behavioral finance started to develop more rapidly after traditional finance

theory failed to explain financial market anomalies like market bubbles and etc. Nevertheless

both theories strongly linked together and could not exist one without another. Traditional

finance theory lacks estimation of psychological factors influencing the market movements

while behavioral finance theory tries to develop a better models for their estimation.

2. Analysis of literature analysis allowed identifying that traditional factors affecting stock price

movements may be divided into micro and macro level factors. Macro-economic factors are

inflation, export, exchange rate, industrial production, currency supply, interest rate,

government performance, BVP growth, investment growth, turnover, domestic credit, narrow,

abroad money growth. Micro-economic factors affecting stock price movements are sales,

equity, debt, liquidity, tobins Q, debt/assets ratio, foreigner control, dividends announcement,

size of the market, number of investors, dividend yield and payout, leverage rates, cash flow,

risk, return ratios, investment duration. Analysis also showed that behavioral factors affecting

stock prices are more and more discussed in scientific papers. Behavioral factors are categorized

in two categories: cognitive bias and emotional bias. Cognitive bias factors are anchoring,

adjustment, framing, conservatism, availability, mental accounting, gamblers fallacy,

representativeness. Emotional bias factors are endowment bias, loss aversion, optimism, status

quo, overconfidence, herding and regret.

3. Political stability, certainty is very necessary to the development of countries and stability of

their financial systems. Political stability is one of the key factors affecting the financial markets

and its instability could influence collapse of the financial market. Political uncertainty lowers

the profits of investors which lead to larger investment in domestic countries and foreign direct

investment loss.

4. To estimate anomalies in market returns exist some empirical models. First and mostly used are

“Event-study” method. “Event-study” model is based on efficient market hypothesis. Authors

adapt this model to estimate how separate events affect the movements of the financial market.

Researchers modify the model by applying more control variables and etc. “Event-study”

method helps to calculate actual returns of the market due to the events. Another adaptation of

the “event-study” method for calculating the anomalies in the market is called GARCH.

60

GARCH method calculates the volatility of the market. Furthermore copula functions and

detrended fluctuation analysis models are also applied for estimation of market anomalies due to

some events. Previous works estimating “BREXIT” event effect for financial markets

determined that UK financial market experienced larger loses in pre-referendum phase and only

recovered a few weeks after it (Shahzad, Rubbaniy, Lensvelt and Bhatti, (2019). Bashir et al.,

(2019) and Guedes, Ferreira, Dionisio and Zebende (2019) also find out negative effect between

UK and other EU countries. Countries with strong trade relations with UK also experienced

negative effect for the financial markets. In this case New Zealand and India experienced it. On

industrial level companies with lower internalization level experienced larger loses than the

ones with higher internalization. Most of the UK economy sectors experienced loses due to the

“BREXIT” vote. Companies strongly related with UK market also experienced loses.

5. This research applied “event-study” method to analyze how “BREXIT” events happened in the

future affected UK financial market and individual companies. Furthermore this research tried

to capture financial “herding” behavior in UK financial market due to the fact that “BREXIT”

events creates political instability and it influences financial “herding” behavior. Empirical

model estimated how different 17 events affected London Stock Exchange and individual 25

companies with largest market capitalization listed on UK financial market indexes.

Furthermore the effect of “BREXIT” events for various sectors were analyzed. The main

question of sectoral analyzes was that financial sector companies were more sensitive to

“BREXIT” news and should influence them negatively. Results showed that “BREXIT” events

were statistically insignificant to London Stock exchange due to the fact that only 5 events out

of 17 were significant in event period of 10 days before and after the event. Additional shorter

event-windows increased significance only for a few events. However “BREXIT” events were

statistically significant to individual companies and results approved of hypothesis that

“BREXIT” events has an effect for them. “BREXIT” events were statistically significant in 2

days before and after the event period and 10 days before the event. The results showed that

larger number of companies experienced loses due to the “BREXIT” events. Calculations of

how various sectors were affected by “BREXIT” events showed that financial sector indeed was

more sensitive than others. However the calculations determined that effect for financial sector

due to the “BREXIT” events were positive and increased financial sector returns. Financial

“herding” behavior could not be detected in the research and due to this fact hypothesis that

financial “herding” behavior is present in UK financial market around “BREXIT” events was

rejected.

61

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66

ANNEXES

67

Annex 1

Research Paper Concepts

“ARCH” – Abbreviation for “Autoregressive Conditional Heteroskedastic“ empirical model.

“CAPM” – Abbreviation for traditional finance theory model „Capital Asset Pricing Model“

“DAX 30” – Abbreviation for the most important Germany’s stock market index which contains the

largest 30 companies of Germany.

“EARCH” – Abbreviation for modified “Autoregressive Conditional Heteroskedastic” empirical

model.

“EPU” – Stands for “Economic Policy Uncertainty” index that measures the stability of countries

economy and etc.

“EU” – Abbreviation for European Union.

“Event” – Political or non-political happening that could have an influence to financial markets or

individual companies.

“Event-window” – number of days in which the effect of the event is calculated

“FTSE 100” – It is the collective name for the 100 largest companies in United Kingdom that are

traded in United Kingdom financial market

“GARCH” – Abbreviation for “Generalized Autoregressive Conditional Heteroskedastic” empirical

model.

“GARCH-M” – Abbreviation for empirical model “Generalized Autoregressive Conditional

Heteroskedastic in Mean”.

“GJR (TARCH)” – Abbreviation for empirical model “Glosten-Jagannathan-Runkle” for volatility

clustering.

“Herding” – Herding is phenomenon where investors follow other investors investing plan,

behavior rather than making their own calculations and analyzes. In other words investor that

follows herding instinct will more likely buy some investment on the fact that others buy and etc.

“IBEX 35” – Abbreviation for the benchmark stock market index of the Bolsa De Madrid, Spain’s

principal Stock exchange

“NYSE” – Abbreviation for New York Stock Exchange.

68

“NZX 50” – Abbreviation for New Zealand Stock Exchange after it changed its name in 2003 from

New Zealand Stock Exchange Limited to New Zealand Exchange Limited.

“RAR” – Abbreviation for “Risk Adjusted Return” empirical model which calculates the abnormal

returns of stocks value influenced by external events.

“S&P 500” – It is a stock market index that tracks the stocks of 500 largest capitalization companies

in the United States of America.

“UK” – Abbreviation for United Kingdom.

“VIX” – Stands for volatility index that was created in Chicago Board Options Exchange. It is the

real-time market index that represents the market's expectation of 30-day forward-looking volatility.

„Brexit“ – United Kingdom withdrawal from European Union. It was announced after the United

Kingdom referendum vote in June 2016.

„CSAD“ – Cross-sectional absolute deviation model. Modified financial „herding“ estimation

model introduced in 2000.

„CSSD“ – Cross-sectional standard deviation model. Model estimates the financial „herding“

behavior in financial markets

69

Annex 2

London Stock Exchange Cumulative Abnormal Returns

6,03% 4,63% 5,50% 4,79% 1,23%

0,248856 (0,0997591)* (0,00303925)*** 0,276945 0,650484

6,20% 3,97% 2,37% -0,26% 6,46%

0,265741 0,135116 0,178874 0,928003 0,0117**

4,61% 3,35% 4,29% 1,36% 3,25%

0,16682 0,196941 (0,0132495)*** 0,604892 0,200054

7,45% 5,35% -3,11% -0,48% 7,93%

(0,043659)*** (0,0369865)*** (0,0687845)*** 0,855614 0,0016**

-4,55% -1,39% 0,06% -1,77% -2,78%

0,232512 0,601939 0,9721 0,517533 0,281653

-0,47% 0,06% -0,35% 0,04% -0,51%

0,901262 0,980913 0,843563 0,988844 0,840769

-7,87% -2,27% -0,85% -2,83% -5,04%

(0,0531224)** 0,389928 0,630902 0,311125 0,081*

6,67% 3,42% 2,06% 6,50% 0,17%

0,100469 0,205196 0,247794 0,03** 0,947214

0,12% 0,39% -0,56% 2,77% -2,66%

0,975552 0,878747 0,746602 0,299905 0,309819

4,67% -0,50% 2,26% 0,92% 3,75%

0,92101 0,853961 0,208713 0,744306 0,936478

1,89% 0,51% 0,39% 1,25% 0,64%

0,613934 0,846852 0,823203 0,640969 0,801278

18,55% 14,03% -2,37% -1,95% 20,50%

(1,63E-10)*** (2,13E-13)*** (0,0595598)* 0,359242 4,95E-27

1,94% 2,18% 3,91% 2,17% -0,24%

0,611527 0,407365 (0,0253674)** 0,430207 0,926349

-6,09% -1,78% 0,07% -3,78% -2,32%

0,151336 0,520397 0,970282 0,244602 0,388166

-8,25% -5,84% -1,69% -6,00% -2,24%

(0,0565578)* (0,0347607)** 0,353532 0,05515** 0,442544

7,22% 7,22% 6,69% -2,07% 9,29%

(0,0573369)** (0,0057379)*** (0,000117712)*** 0,451812 0,000278***

4,69% 1,14% 1,57% 1,64% 3,05%

0,230402 0,666978 0,37309 0,565029 0,242777

„London Stock Exchange“ Abnormal Returns

CAR (-10;0) CAR (0;+10)

2018-11-25

Date CAR (-10;+10) CAR (-5;+5) CAR (-2;+2)

2016-07-16

2017-01-17

2017-03-29

2017-06-08

2017-12-08

2018-07-06

2019-10-02

2019-10-19

2019-12-12

2020-01-30

2019-01-15

2019-03-12

2019-04-12

2019-06-24

2019-07-24

2019-09-04

70

Annex 3

Individual Companies Cumulated Abnormal Returns of period (T= -10:+10) (Part 1)

Event number 1 2 3 4 5 6

Cumulated Average

Abnormal Returns of

companies on event period

-0,53% -0,24% -0,18% 0,05% -0,28% 0,12%

CAR (-10;+10)

ROYAL DUTCH SHELL

PLC -11,31% -3,40% 2,36% -1,28% 2,04% 3,38%

HSBC HOLDINGS PLC 0,03% 4,13% -1,66% 3,74% 2,24% 0,39%

ASTRAZENECA PLC 9,53% -6,61% -3,78% 6,22% -4,23% 3,79%

BP PLC -8,60% -6,94% 3,59% -3,15% 0,06% -0,70%

GLAXOSMITHKLINE

PLC 4,45% -2,02% -2,83% 5,97% -2,48% -0,80%

BRITISH AMERICAN

TOBACCO PLC -2,67% 6,71% 4,43% 1,69% -4,04% 3,81%

DIAGEO PLC 3,55% 4,20% -0,32% 0,51% 0,58% 3,87%

UNILEVER PLC -0,84% -1,93% 0,30% 2,75% -4,38% 5,47%

RIO TINTO PLC 3,78% 10,11% -6,07% -5,17% -1,79% -3,41%

RECKITT BENCKISER

GROUP PLC -2,32% -0,96% 2,44% 3,64% 4,76% 4,21%

VODAFONE GROUP PLC 0,32% -0,60% 1,67% 2,13% 4,21% -3,68%

LLOYDS BANKING

GROUP PLC -7,53% 5,59% -7,60% -5,86% 0,91% 1,26%

RELX PLC 0,59% -3,13% 1,75% 2,05% -5,52% 3,72%

NATIONAL GRID PLC -0,05% -1,64% 6,54% -6,43% -0,89% 2,15%

PRUDENTIAL PLC -3,30% -6,55% -4,15% 3,05% -4,34% -3,12%

BARCLAYS PLC 7,27% 0,97% -3,76% -6,45% 6,64% -1,58%

ROYAL BANK OF

SCOTLAND GROUP PLC 3,21% 0,93% 1,76% -5,19% 1,93% -6,07%

TESCO PLC -13,98% -6,31% -2,98% -

10,00% 4,22% -0,80%

EXPERIAN PLC 1,92% -3,89% -1,72% -3,93% -0,87% 2,79%

CRH PLC 0,62% -2,12% -4,03% 1,30% -3,03% -4,06%

ASSOCIATED BRITISH

FOODS PLC -1,20%

-

13,55% 0,41% 0,84%

-

10,54%

-

16,65%

BAE SYSTEMS PLC 0,18% -1,49% 1,12% 3,86% 2,12% 6,89%

STANDARD

CHARTERED PLC -0,56% 17,49% -0,44% 3,07% 2,46% -4,77%

IMPERIAL BRANDS PLC -1,88% 6,08% 2,92% 0,42% -0,77% 9,11%

LEGAL & GENERAL

GROUP PLC 5,61% -1,20% 5,54% 7,58% 3,62% -2,22%

71

Individual Companies Cumulated Abnormal Returns of period (T= -10:+10) (Part 2)

Event number 7 8 9 10 11 12

Cumulated Average

Abnormal Returns of

companies on event period

-1,81% 1,24% 0,04% 1,04% -0,08% 0,42%

CAAR (-10;+10)

ROYAL DUTCH SHELL

PLC 2,07% -5,17% 0,08% -0,60% -1,37% -4,65%

HSBC HOLDINGS PLC 6,78% -2,27% -1,21% 5,88% 0,29% -1,73%

ASTRAZENECA PLC 0,16% -

11,33% 0,53%

-

16,17% 3,23% 11,73%

BP PLC 4,91% -2,02% 2,34% -1,89% -5,11% -1,61%

GLAXOSMITHKLINE PLC -2,59% -5,33% 1,35% -3,45% 1,37% 4,59%

BRITISH AMERICAN

TOBACCO PLC

-

14,75% -1,96% 6,01% -7,98% -1,38% 5,49%

DIAGEO PLC 5,37% -8,24% 3,65% 0,84% -1,35% 0,26%

UNILEVER PLC 5,79% -3,75% 4,27% 3,85% 0,83% -1,12%

RIO TINTO PLC -2,42% 5,42% -3,86% -2,41% 1,52% -9,66%

RECKITT BENCKISER

GROUP PLC 4,29% -5,32% 8,84% -5,12% -2,54% -4,50%

VODAFONE GROUP PLC 17,36% -

13,55% 3,56% 1,69% 1,91% 15,29%

LLOYDS BANKING

GROUP PLC -8,45% 9,97% 2,49% -0,38% -1,56% -7,48%

RELX PLC 5,03% -1,28% -9,65% 5,28% 0,04% -0,21%

NATIONAL GRID PLC -1,16% 4,62% 3,44% -3,38% 1,95% 2,22%

PRUDENTIAL PLC -6,57% 3,22% -3,85% 8,31% 3,35% -9,81%

BARCLAYS PLC -6,36% 7,88% -2,02% 6,51% 2,81% 1,22%

ROYAL BANK OF

SCOTLAND GROUP PLC

-

15,79% 11,13% -3,43% -3,73% 4,51%

-

12,01%

TESCO PLC -8,41% 12,50% 1,46% 6,53% -0,24% -5,71%

EXPERIAN PLC 5,71% -0,51% -2,78% 4,42% -4,14% 7,49%

CRH PLC -6,94% 3,24% -6,11% 7,44% -0,78% 2,96%

ASSOCIATED BRITISH

FOODS PLC

-

15,42% 13,27% 4,48% 4,63% -7,09% 3,60%

BAE SYSTEMS PLC -

11,22% 8,95% -6,28% 2,69% -0,59% 16,53%

STANDARD CHARTERED

PLC 9,97% 3,12% -6,65% 16,44% 3,33% -9,69%

IMPERIAL BRANDS PLC -8,03% 2,37% 0,53% -7,88% -4,37% 9,86%

LEGAL & GENERAL

GROUP PLC -4,59% 6,05% 3,77% 4,50% 3,28% -2,62%

72

Individual Companies Cumulated Abnormal Returns of period (T= -10:+10) (Part 3)

Event number 13 14 15 16 17

Cumulated Average

Abnormal Returns of

companies on event period 0,78% -0,16% 0,49% 0,46% 1,10%

CAAR (-10;+10)

ROYAL DUTCH SHELL

PLC -0,96% -0,89% -4,07% -2,10% -12,32%

HSBC HOLDINGS PLC -0,24% 1,23% -3,45% 0,32% -4,46%

ASTRAZENECA PLC -7,37% -0,86% 3,30% -1,21% -2,22%

BP PLC 0,82% -3,44% -0,71% -5,09% -3,52%

GLAXOSMITHKLINE PLC -2,75% 0,86% 0,55% 0,13% -7,11%

BRITISH AMERICAN

TOBACCO PLC -4,44% -7,46% -3,50% 3,55% -1,59%

DIAGEO PLC -7,03% -2,70% -7,69% -1,61% -5,46%

UNILEVER PLC -4,57% -6,01% -6,79% -6,07% 7,21%

RIO TINTO PLC 1,82% -5,77% 1,69% 2,63% -4,52%

RECKITT BENCKISER

GROUP PLC 0,13% -3,31% -7,30% 0,85% 3,67%

VODAFONE GROUP PLC 3,57% 6,08% 0,34% -8,19% 0,11%

LLOYDS BANKING

GROUP PLC 5,18% 15,97% 8,81% 1,06% 4,49%

RELX PLC -6,48% -4,90% -4,77% -1,69% 3,89%

NATIONAL GRID PLC -3,15% 8,06% 1,77% 2,61% 8,71%

PRUDENTIAL PLC 1,18% -11,62% -5,42% 0,13% 8,30%

BARCLAYS PLC 5,33% 13,90% 15,22% 3,13% 3,75%

ROYAL BANK OF

SCOTLAND GROUP PLC 10,66% 15,16% 9,54% 4,33% -0,19%

TESCO PLC 5,23% 4,19% -1,62% 6,57% 2,56%

EXPERIAN PLC -4,92% -2,90% -9,82% -3,24% 7,18%

CRH PLC 0,34% 2,01% 3,29% -1,97% 3,24%

ASSOCIATED BRITISH

FOODS PLC -0,49% -2,77% 3,47% -0,59% 5,65%

BAE SYSTEMS PLC 4,59% -5,30% 4,04% -3,86% 10,20%

STANDARD CHARTERED

PLC 8,67% -1,08% 9,37% 0,48% -6,44%

IMPERIAL BRANDS PLC 2,37% -11,73% -5,60% 8,67% -4,75%

LEGAL & GENERAL

GROUP PLC 12,07% -0,82% 11,66% 12,72% 11,07%

73

Annex 4

Individual Companies Cumulated Abnormal Returns of period (T= -5:+5) (Part 1)

Event 1 2 3 4 5 6

Cumulated Average

Abnormal Returns of

companies on event period

0,12% 0,13% -0,43% 0,49% -0,17% -0,07%

CAAR (-5;5)

ROYAL DUTCH SHELL

PLC -4,02% -0,87% 1,04% 0,95% 0,30% -0,70%

BP PLC -6,23% -3,20% 2,40% 0,43% 0,28% -2,85%

HSBC HOLDINGS PLC 2,60% 2,09% 0,80% 3,29% 0,47% -0,05%

LLOYDS BANKING

GROUP PLC 1,37% 0,34% -4,37% -1,79% -1,22% -0,35%

PRUDENTIAL PLC 6,20% -1,84% -4,53% 3,52% -4,43% -1,78%

BARCLAYS PLC 7,35% 0,09% -3,38% -0,92% 3,24% 0,26%

ROYAL BANK OF

SCOTLAND GROUP PLC 11,58% -1,41% 2,00% 1,46% -1,13% -3,36%

STANDARD CHARTERED

PLC 3,03% 11,25% 3,75% 4,88% 0,31% -3,80%

LEGAL & GENERAL

GROUP PLC 11,01% -0,87% 0,12% 6,61% 0,91% -0,01%

ASTRAZENECA PLC -0,18% -8,76% 0,39% 1,33% -0,18% 4,11%

GLAXOSMITHKLINE PLC -1,23% -3,82% -0,72% -0,06% -0,75% 3,83%

BRITISH AMERICAN

TOBACCO PLC -3,71% 3,86% 3,20% -0,44% 3,58% 0,58%

DIAGEO PLC -2,03% 0,33% -1,34% 0,70% 1,47% 1,78%

UNILEVER PLC -4,56% 0,82% -2,03% -1,22% -0,82% 0,30%

RECKITT BENCKISER

GROUP PLC -3,56% 0,64% -1,95% 0,14% 2,25% 3,89%

TESCO PLC -2,32% -4,07% -2,63% -1,85% 4,90% -1,51%

ASSOCIATED BRITISH

FOODS PLC -5,47% -6,12% -4,30% 0,09% -5,43% -11,15%

IMPERIAL BRANDS PLC -1,49% 2,76% 1,56% 0,03% -0,41% 2,47%

EXPERIAN PLC -0,43% -2,22% -1,55% 0,44% 1,11% 1,02%

CRH PLC 1,89% 3,72% -0,85% 1,60% -4,33% -0,59%

BAE SYSTEMS PLC -2,55% 0,92% -1,03% -0,24% -0,08% 5,43%

RIO TINTO PLC -3,57% 15,55% -1,46% 0,28% 0,04% -4,79%

VODAFONE GROUP PLC 2,19% -2,23% 0,00% -2,44% 1,39% -0,88%

RELX PLC -2,41% -2,92% 2,00% 1,84% -2,61% 4,41%

NATIONAL GRID PLC -0,53% -0,74% 2,07% -6,37% -3,00% 2,09%

74

Individual Companies Cumulated Abnormal Returns of period (T= -5:+5) (Part 2)

Event 7 8 9 10 11 12

Cumulated Average

Abnormal Returns of

companies on event period

-0,18% 1,47% 0,36% 0,14% -0,51% -0,03%

CAAR (-5;5)

ROYAL DUTCH SHELL

PLC -0,41% -3,90% -0,15% 1,27% 0,70% -0,09%

BP PLC 0,50% -3,41% 0,60% 1,81% 0,66% 0,22%

HSBC HOLDINGS PLC 3,57% -1,19% -1,45% 1,64% 0,72% -1,97%

LLOYDS BANKING

GROUP PLC 2,17% 6,53% 1,19% 3,27% -2,31% -8,13%

PRUDENTIAL PLC -0,98% 4,99% -3,95% 6,07% 4,55% -4,32%

BARCLAYS PLC -0,72% 4,70% 1,12% 0,54% 1,26% -1,59%

ROYAL BANK OF

SCOTLAND GROUP PLC 1,45% 6,53% -0,02% 1,71% 3,66% -5,11%

STANDARD CHARTERED

PLC 5,79% 3,63% -4,98% 3,52% 3,41% -6,09%

LEGAL & GENERAL

GROUP PLC 1,73% 4,43% 1,62% 3,31% 2,09% 1,04%

ASTRAZENECA PLC -2,03% -9,53% -0,09% -5,47% 1,88% 8,92%

GLAXOSMITHKLINE PLC -4,94% -2,86% -2,62% -3,45% -1,38% 2,07%

BRITISH AMERICAN

TOBACCO PLC 2,14% 0,84% 8,27% -2,94% -2,86% -0,16%

DIAGEO PLC 0,05% -0,06% 1,32% 0,83% -3,09% 0,25%

UNILEVER PLC -1,22% -3,09% 4,58% 1,72% -2,74% -2,96%

RECKITT BENCKISER

GROUP PLC 0,32% 0,70% 3,67% -9,25% -7,22% -4,34%

TESCO PLC -4,11% 7,37% 1,33% 5,55% 1,45% -7,36%

ASSOCIATED BRITISH

FOODS PLC -2,63% 9,51% 1,48% 0,06% -3,18% 4,64%

IMPERIAL BRANDS PLC -8,64% 5,48% 1,99% -3,62% -5,54% 2,89%

EXPERIAN PLC 2,32% 1,40% -1,23% 3,47% -2,89% 3,67%

CRH PLC 2,54% 2,11% -5,60% 1,79% 0,75% 0,13%

BAE SYSTEMS PLC -6,11% 8,13% -0,51% -2,22% 0,00% 5,85%

RIO TINTO PLC -6,90% -1,72% -6,10% -1,58% -0,07% -5,06%

VODAFONE GROUP PLC 9,49% -6,61% 8,74% -2,60% -1,09% 16,52%

RELX PLC 1,67% 0,82% -3,00% 1,52% -1,22% -1,61%

NATIONAL GRID PLC 0,53% 2,03% 2,72% -3,32% -0,29% 1,95%

75

Individual Companies Cumulated Abnormal Returns of period (T= -5:+5) (Part 3)

Event 13 14 15 16 17

Cumulated Average

Abnormal Returns of

companies on event period

0,87% -1,39% 0,20% 0,81% 1,15%

CAAR (-5;5)

ROYAL DUTCH SHELL

PLC -1,73% 1,78% 0,30% -1,83% -7,39%

BP PLC 0,26% 1,31% 2,15% -4,77% 0,26%

HSBC HOLDINGS PLC 4,26% 0,89% -3,07% 2,24% -0,22%

LLOYDS BANKING

GROUP PLC 1,91% -3,79% -0,93% 0,27% -1,14%

PRUDENTIAL PLC 4,66% -15,29% 6,18% 1,26% 3,56%

BARCLAYS PLC 5,78% 1,52% 5,94% 4,76% 3,25%

ROYAL BANK OF

SCOTLAND GROUP PLC 6,20% -5,24% 2,23% 7,15% 2,49%

STANDARD CHARTERED

PLC 8,13% -3,40% 4,70% 2,46% -6,06%

LEGAL & GENERAL

GROUP PLC 14,02% -4,06% 4,83% 8,86% 4,60%

ASTRAZENECA PLC -7,81% -1,56% 5,79% -0,23% 0,46%

GLAXOSMITHKLINE PLC -5,21% 2,45% 2,55% 1,18% -6,62%

BRITISH AMERICAN

TOBACCO PLC 3,90% 0,97% -1,06% 4,28% 3,39%

DIAGEO PLC -5,07% 3,79% -4,76% -1,62% -0,39%

UNILEVER PLC -4,84% -0,45% -2,94% -7,98% 7,26%

RECKITT BENCKISER

GROUP PLC -0,25% -0,86% -5,72% -2,87% 7,56%

TESCO PLC 6,80% -4,64% -2,14% 7,44% 5,18%

ASSOCIATED BRITISH

FOODS PLC -3,91% -7,49% 0,79% -0,08% 2,44%

IMPERIAL BRANDS PLC 5,02% -6,36% -4,23% 3,15% -3,83%

EXPERIAN PLC -6,29% 1,05% -4,53% -2,75% 4,73%

CRH PLC -0,53% -0,32% 3,25% -3,15% 3,88%

BAE SYSTEMS PLC 2,98% 0,03% 0,56% -1,69% 2,65%

RIO TINTO PLC 3,70% -1,16% -3,52% -0,07% -6,33%

VODAFONE GROUP PLC 2,68% 3,16% -0,96% -0,74% 0,64%

RELX PLC -7,57% 0,13% -0,97% -0,24% 4,26%

NATIONAL GRID PLC -5,28% 2,79% 0,58% 5,15% 4,21%

76

Annex 5

Individual Companies Cumulated Abnormal Returns of period (T= -2:+2) (Part 1)

Event 1 2 3 4 5 6

Cumulated Average

Abnormal Returns of

companies on event period -0,05% 0,04% -0,17% 0,14% 0,19% -0,79%

CAR (-2:+2)

ROYAL DUTCH SHELL

PLC -1,67% -1,79% 0,95% 2,29% -0,51% 1,72%

BP PLC -2,21% -1,67% 1,54% 1,63% 0,74% -0,35%

HSBC HOLDINGS PLC 3,48% 2,18% 0,69% 1,82% 1,26% -0,76%

LLOYDS BANKING

GROUP PLC 2,15% -0,43% -2,48% -0,01% 1,05% -0,41%

PRUDENTIAL PLC 2,27% -1,10% -2,10% 1,74% -2,58% -1,33%

BARCLAYS PLC 3,55% 0,04% -0,15% -2,47% 2,57% -0,56%

ROYAL BANK OF

SCOTLAND GROUP PLC 8,80% 2,10% 1,87% -3,28% 0,82% -2,65%

STANDARD CHARTERED

PLC 2,22% 7,94% 5,40% 3,97% 0,47% -1,00%

LEGAL & GENERAL

GROUP PLC 5,29% -0,26% 0,63% 2,90% -0,14% -0,30%

ASTRAZENECA PLC 0,85% -1,42% -0,51% -1,23% 2,39% 0,25%

GLAXOSMITHKLINE PLC 0,54% 0,04% -0,59% 0,40% 0,78% 0,24%

BRITISH AMERICAN

TOBACCO PLC -1,15% 1,68% 0,69% -1,98% 1,73% -0,67%

DIAGEO PLC -1,37% -0,01% -1,09% -1,10% -0,15% 0,43%

UNILEVER PLC -1,32% -0,29% -1,94% -1,19% -0,31% -1,52%

RECKITT BENCKISER

GROUP PLC -2,10% 1,18% -0,62% -0,66% 3,64% -0,95%

TESCO PLC -5,78% -1,15% -2,99% 0,52% 0,71% -2,52%

ASSOCIATED BRITISH

FOODS PLC -0,29% 1,30% -0,71% -1,43% -2,80% -9,31%

IMPERIAL BRANDS PLC -0,30% 0,24% 1,95% -0,87% 0,59% 0,36%

EXPERIAN PLC -0,70% -3,28% -0,67% 1,63% 1,41% 0,61%

CRH PLC -1,76% -0,22% -0,33% 1,40% -2,39% -0,54%

BAE SYSTEMS PLC -0,83% -1,64% -1,53% -0,56% -1,27% 0,11%

RIO TINTO PLC -6,42% 2,94% -1,46% 4,18% -1,24% 0,22%

VODAFONE GROUP PLC -1,93% -2,44% -1,09% -2,13% 1,34% 0,75%

RELX PLC -1,75% -2,30% 0,04% 0,07% -2,48% -0,06%

NATIONAL GRID PLC -0,91% -0,64% 0,38% -2,07% -0,95% -1,37%

77

Individual Companies Cumulated Abnormal Returns of period (T= -2:+2) (Part 2)

Event 7 8 9 10 11 12

Cumulated Average Abnormal

Returns of companies on event

period 0,01% 0,77% 0,12% 0,34% -0,59% 0,07%

CAR (-2:+2)

ROYAL DUTCH SHELL PLC -1,92% -1,87% 0,15% -1,25% 2,40% -0,31%

BP PLC -0,41% -1,01% 1,18% -1,84% 2,10% 0,30%

HSBC HOLDINGS PLC 2,20% -0,30% -0,89% 0,71% -1,21% -0,46%

LLOYDS BANKING GROUP

PLC 0,72% 4,45% 2,12% 3,88% -3,76% -1,85%

PRUDENTIAL PLC 0,26% 1,95% 0,68% 4,13% 0,13% -2,45%

BARCLAYS PLC 1,24% 2,89% 1,70% 3,50% -2,26% 2,75%

ROYAL BANK OF

SCOTLAND GROUP PLC 2,32% 7,35% 2,04% 3,06% -1,70% -0,18%

STANDARD CHARTERED

PLC 3,96% -0,75% -1,89% 3,41% -0,47% -3,74%

LEGAL & GENERAL

GROUP PLC 3,68% 3,45% 4,40% 2,56% 0,49% 2,19%

ASTRAZENECA PLC -1,20% -6,43% 0,23% -3,08% -1,74% 7,06%

GLAXOSMITHKLINE PLC 1,03% -2,42% -0,42% -2,32% -1,31% 1,91%

BRITISH AMERICAN

TOBACCO PLC 1,09% 1,66% -2,98% -0,10% 0,08% -2,94%

DIAGEO PLC 0,22% 0,89% -0,25% -0,97% -0,17% -2,43%

UNILEVER PLC -0,14% -0,87% 1,45% -1,49% -0,91% -2,67%

RECKITT BENCKISER

GROUP PLC -1,49% -2,77% 1,53% -9,19% -4,88% -2,00%

TESCO PLC -4,76% 2,04% -3,01% 6,02% -5,00% -5,86%

ASSOCIATED BRITISH

FOODS PLC -1,86% 5,08% 0,32% 1,69% 0,63% 1,81%

IMPERIAL BRANDS PLC -4,72% 0,88% -2,47% -1,78% -0,31% -0,31%

EXPERIAN PLC 3,34% 0,92% -1,83% 1,29% 0,05% 2,19%

CRH PLC 0,44% -0,33% -1,89% 1,84% 1,07% 1,31%

BAE SYSTEMS PLC -4,80% 2,57% -0,85% 0,55% -0,21% 1,54%

RIO TINTO PLC -7,04% 2,29% -0,18% -1,94% 3,38% -6,14%

VODAFONE GROUP PLC 7,59% -1,77% 3,35% 1,32% -0,89% 11,97%

RELX PLC 1,96% 1,12% -0,11% 0,25% 0,33% -1,12%

NATIONAL GRID PLC -1,41% 0,31% 0,65% -1,83% -0,46% 1,18%

78

Individual Companies Cumulated Abnormal Returns of period (T= -2:+2) (Part 3)

Event 13 14 15 16 17

Cumulated Average

Abnormal Returns of

companies on event period 0,61% -0,39% 0,09% 1,13% 0,61%

CAR (-2:+2)

ROYAL DUTCH SHELL

PLC -1,97% 0,96% 0,81% -4,95% -7,39%

BP PLC -1,20% 0,11% 2,34% -5,97% -3,50%

HSBC HOLDINGS PLC 0,70% 0,16% 0,01% 0,70% 0,64%

LLOYDS BANKING

GROUP PLC -0,66% -2,59% -2,07% 3,93% 0,01%

PRUDENTIAL PLC 0,89% 0,37% 12,50% 3,44% 3,05%

BARCLAYS PLC 1,90% -0,97% 0,05% 7,78% 1,21%

ROYAL BANK OF

SCOTLAND GROUP PLC 0,99% -3,38% 1,00% 9,86% 0,90%

STANDARD CHARTERED

PLC 3,26% -1,83% 3,23% 1,54% -2,58%

LEGAL & GENERAL

GROUP PLC 6,91% -1,36% 0,60% 8,50% 2,66%

ASTRAZENECA PLC -1,96% -2,94% 0,28% -0,80% -0,06%

GLAXOSMITHKLINE PLC -0,71% 1,42% 1,69% -1,19% 1,87%

BRITISH AMERICAN

TOBACCO PLC 0,77% -1,65% -0,39% 2,10% 1,86%

DIAGEO PLC -2,14% 2,12% -2,32% -1,58% -2,38%

UNILEVER PLC -0,81% -0,10% -1,88% -0,89% 4,06%

RECKITT BENCKISER

GROUP PLC -0,93% -3,73% -2,15% 1,24% 4,47%

TESCO PLC 5,11% -1,01% -1,46% 3,86% 1,97%

ASSOCIATED BRITISH

FOODS PLC 2,81% -1,29% -1,56% 1,20% 0,87%

IMPERIAL BRANDS PLC 0,01% 5,97% -0,15% 3,33% 2,32%

EXPERIAN PLC 3,02% 0,75% -4,53% -2,19% 1,95%

CRH PLC -1,16% 0,43% 0,27% -2,21% 2,26%

BAE SYSTEMS PLC 3,79% -1,49% -4,60% 1,04% 2,09%

RIO TINTO PLC 0,55% -0,50% 0,80% -1,82% -1,88%

VODAFONE GROUP PLC -0,21% 1,27% 0,46% -0,26% -0,77%

RELX PLC -1,11% -1,59% -2,87% -0,77% 0,39%

NATIONAL GRID PLC -2,61% 1,05% 2,29% 2,32% 1,17%

79

Annex 6

Individual Companies Cumulated Abnormal Returns of period (T= -10:0) (Part 1)

Event 1 2 3 4 5 6

Cumulated Average

Abnormal Returns of

companies on event period -0,42% -0,23% 0,41% 0,32% 0,39% 0,22%

CAR (-10:0)

ROYAL DUTCH SHELL

PLC -2,24% -1,33% 1,71% -1,88% 1,32% 2,82%

BP PLC -1,38% -3,07% 0,67% -3,31% -1,25% 2,29%

HSBC HOLDINGS PLC 2,28% 0,77% -2,03% 3,46% 0,79% -0,85%

LLOYDS BANKING

GROUP PLC -3,09% 3,99% -2,41% -1,02% 2,68% 1,45%

PRUDENTIAL PLC -4,16% -5,89% -1,40% 0,97% -2,76% -2,99%

BARCLAYS PLC 4,83% 3,52% 1,12% -2,77% 5,52% -1,80%

ROYAL BANK OF

SCOTLAND GROUP PLC 1,99% -2,01% 3,34% -1,83% 5,28% -3,38%

STANDARD CHARTERED

PLC 2,77% 10,87% 3,28% 4,57% 4,11% -1,86%

LEGAL & GENERAL

GROUP PLC -1,96% -0,27% 1,92% 4,16% 1,44% -2,32%

ASTRAZENECA PLC -1,24% 0,54% 1,16% 2,99% -4,90% -2,25%

GLAXOSMITHKLINE PLC 1,64% -1,73% -0,95% 3,47% -1,31% -0,17%

BRITISH AMERICAN

TOBACCO PLC -3,02% -1,60% 2,79% 1,99% -2,10% 0,82%

DIAGEO PLC 1,51% -0,01% 0,24% -1,52% 0,67% -0,20%

UNILEVER PLC 0,50% 0,05% -0,59% 2,02% -1,25% 2,51%

RECKITT BENCKISER

GROUP PLC -1,16% -2,54% -0,34% 3,40% 4,80% 3,32%

TESCO PLC -6,57% -3,30% 0,41% -1,78% 5,09% 0,62%

ASSOCIATED BRITISH

FOODS PLC 1,31% -7,34% -0,11% 1,12% -6,14% -12,57%

IMPERIAL BRANDS PLC -1,02% 0,07% -0,64% -1,54% -0,69% 8,05%

EXPERIAN PLC 1,50% -1,61% -1,75% -3,84% 0,87% 2,43%

CRH PLC -2,87% -2,89% -1,90% 0,04% -0,55% -1,88%

BAE SYSTEMS PLC 0,88% 1,12% 1,13% 3,30% 2,59% 2,63%

RIO TINTO PLC 3,22% 5,88% -2,51% 1,06% -5,54% -2,32%

VODAFONE GROUP PLC -2,39% 3,65% 3,65% -1,54% 3,04% 3,58%

RELX PLC -1,75% -2,33% 0,38% 1,25% -3,75% 1,51%

NATIONAL GRID PLC 0,00% -0,36% 3,17% -4,76% 1,83% 6,00%

80

Individual Companies Cumulated Abnormal Returns of period (T= -10:0) (Part 2)

Event 7 8 9 10 11 12

Cumulated Average

Abnormal Returns of

companies on event period -0,95% 0,56% 0,56% 0,57% -0,94% 0,07%

CAR (-10:0)

ROYAL DUTCH SHELL

PLC -1,15% 0,08% -1,05% 0,19% 1,14% -1,23%

BP PLC 1,62% 0,68% 0,58% -0,15% -1,87% -3,65%

HSBC HOLDINGS PLC 7,41% -3,39% 1,47% 4,04% -1,66% -1,46%

LLOYDS BANKING

GROUP PLC -3,65% 6,10% 4,04% 2,46% -1,54% -0,58%

PRUDENTIAL PLC -1,12% 0,27% -3,41% 4,74% 1,44% -2,25%

BARCLAYS PLC -2,65% 4,45% 3,78% 6,23% -1,84% 3,81%

ROYAL BANK OF

SCOTLAND GROUP PLC -10,12% 6,03% 0,96% 3,69% 0,32% 1,15%

STANDARD CHARTERED

PLC 9,67% -0,23% 0,02% 11,43% -0,44% -6,67%

LEGAL & GENERAL

GROUP PLC -3,41% 0,37% 3,37% 5,12% -0,14% 2,74%

ASTRAZENECA PLC -0,49% -7,73% -0,44% -11,08% 5,11% -2,11%

GLAXOSMITHKLINE PLC 0,83% -2,03% -1,24% -4,90% 0,72% 1,12%

BRITISH AMERICAN

TOBACCO PLC -19,00% -4,88% 6,75% -4,76% -9,24% 2,24%

DIAGEO PLC 2,86% -3,26% 1,53% -2,76% -0,39% -2,89%

UNILEVER PLC 1,76% -2,81% -0,26% -2,70% -0,12% -0,70%

RECKITT BENCKISER

GROUP PLC 7,16% 1,25% 5,12% -9,99% -1,57% 1,04%

TESCO PLC -8,55% 10,97% 2,03% 5,61% -0,27% -2,67%

ASSOCIATED BRITISH

FOODS PLC -5,24% 5,91% -0,02% 2,95% -5,14% 0,90%

IMPERIAL BRANDS PLC -10,26% 1,35% 2,46% -6,09% -11,40% 8,80%

EXPERIAN PLC 3,21% -3,42% -1,70% 3,34% 0,68% 2,31%

CRH PLC -2,68% 2,09% -0,68% 5,50% -2,77% -0,04%

BAE SYSTEMS PLC -2,71% 6,90% -1,60% 5,60% 0,79% 9,19%

RIO TINTO PLC -5,12% 0,16% -5,89% 4,34% 3,12% -4,10%

VODAFONE GROUP PLC 15,51% -5,47% 1,22% -0,43% -3,04% -0,33%

RELX PLC 5,18% -2,07% -6,37% -2,13% 2,20% -1,75%

NATIONAL GRID PLC -2,68% 2,62% 3,35% -6,03% 2,33% -0,99%

81

Individual Companies Cumulated Abnormal Returns of period (T= -10:0) (Part 3)

Event 13 14 15 16 17

Cumulated Average

Abnormal Returns of

companies on event period 0,26% -0,51% 1,21% 0,36% 1,44%

CAR (-10:0)

ROYAL DUTCH SHELL

PLC -2,98% 2,30% -3,64% 0,49% -6,33%

BP PLC -0,80% -1,36% -1,09% -1,26% -0,78%

HSBC HOLDINGS PLC -1,33% 1,51% 0,58% 0,86% -1,02%

LLOYDS BANKING

GROUP PLC -2,32% -0,66% 16,80% 2,00% 4,73%

PRUDENTIAL PLC -3,08% -1,01% -2,81% 1,22% 2,11%

BARCLAYS PLC -3,07% 0,67% 15,73% 1,90% 0,96%

ROYAL BANK OF

SCOTLAND GROUP PLC -0,76% -1,59% 22,52% 2,99% 2,21%

STANDARD CHARTERED

PLC 2,55% -1,60% 4,56% 5,26% -3,25%

LEGAL & GENERAL

GROUP PLC 0,72% 0,83% 15,27% 7,21% 10,43%

ASTRAZENECA PLC -0,46% 1,45% -4,64% -2,56% -2,37%

GLAXOSMITHKLINE PLC 3,21% 3,54% -3,76% 0,68% 0,29%

BRITISH AMERICAN

TOBACCO PLC -6,15% 0,55% -5,90% -0,82% -1,52%

DIAGEO PLC 3,19% -0,24% -6,49% -3,01% -5,84%

UNILEVER PLC 3,00% -1,60% -5,53% 0,55% 6,07%

RECKITT BENCKISER

GROUP PLC 3,55% -1,36% -5,60% 1,25% 3,73%

TESCO PLC 2,13% 3,55% 2,90% 3,89% 1,80%

ASSOCIATED BRITISH

FOODS PLC 1,66% -4,64% 0,87% -4,28% 5,84%

IMPERIAL BRANDS PLC 1,14% -12,73% 3,30% 1,14% -1,18%

EXPERIAN PLC 0,33% 1,14% -9,40% -2,86% 4,58%

CRH PLC 2,69% 0,44% 1,63% 1,47% 1,93%

BAE SYSTEMS PLC -0,72% -5,26% -1,90% 0,51% 6,50%

RIO TINTO PLC 0,87% -3,12% -1,39% 5,06% -4,12%

VODAFONE GROUP PLC 3,69% 2,70% 1,96% -7,80% -0,72%

RELX PLC 0,41% -0,91% -7,07% -2,64% 3,38%

NATIONAL GRID PLC -0,98% 4,56% 3,34% -2,13% 8,66%

82

Annex 7

Individual Companies Cumulated Abnormal Returns of period (T= 0:+10) (Part 1)

Event 1 2 3 4 5 6

Cumulated Average

Abnormal Returns of

companies on event period -0,11% -0,01% -0,59% -0,27% -0,67% -0,10%

CAR (0:+10

ROYAL DUTCH SHELL

PLC -9,07% -2,07% 0,65% 0,60% 0,72% 0,57%

BP PLC -7,22% -3,87% 2,92% 0,17% 1,31% -2,98%

HSBC HOLDINGS PLC -2,24% 3,36% 0,37% 0,28% 1,45% 1,25%

LLOYDS BANKING

GROUP PLC -4,44% 1,60% -5,20% -4,85% -1,77% -0,18%

PRUDENTIAL PLC 0,86% -0,66% -2,75% 2,08% -1,58% -0,13%

BARCLAYS PLC 2,44% -2,55% -4,88% -3,68% 1,12% 0,23%

ROYAL BANK OF

SCOTLAND GROUP PLC 1,22% 2,94% -1,58% -3,36% -3,34% -2,69%

STANDARD CHARTERED

PLC -3,33% 6,62% -3,72% -1,50% -1,65% -2,91%

LEGAL & GENERAL

GROUP PLC 7,57% -0,93% 3,63% 3,42% 2,18% 0,09%

ASTRAZENECA PLC 10,77% -7,15% -4,95% 3,23% 0,67% 6,04%

GLAXOSMITHKLINE PLC 2,81% -0,29% -1,88% 2,50% -1,17% -0,63%

BRITISH AMERICAN

TOBACCO PLC 0,35% 8,32% 1,63% -0,30% -1,94% 3,00%

DIAGEO PLC 2,04% 4,21% -0,56% 2,03% -0,09% 4,06%

UNILEVER PLC -1,33% -1,97% 0,89% 0,73% -3,12% 2,96%

RECKITT BENCKISER

GROUP PLC -1,16% 1,58% 2,78% 0,24% -0,04% 0,89%

TESCO PLC -7,41% -3,01% -3,39% -8,23% -0,87% -1,42%

ASSOCIATED BRITISH

FOODS PLC -2,51% -6,20% 0,51% -0,28% -4,41% -4,08%

IMPERIAL BRANDS PLC -0,85% 6,00% 3,56% 1,96% -0,08% 1,06%

EXPERIAN PLC 0,42% -2,27% 0,03% -0,09% -1,74% 0,36%

CRH PLC 3,49% 0,78% -2,12% 1,26% -2,48% -2,18%

BAE SYSTEMS PLC -0,70% -2,61% -0,02% 0,56% -0,47% 4,26%

RIO TINTO PLC 0,56% 4,23% -3,56% -6,23% 3,76% -1,10%

VODAFONE GROUP PLC 2,71% -4,24% -1,98% 3,68% 1,17% -7,26%

RELX PLC 2,34% -0,79% 1,37% 0,80% -1,76% 2,21%

NATIONAL GRID PLC -0,06% -1,28% 3,37% -1,68% -2,72% -3,86%

83

Individual Companies Cumulated Abnormal Returns of period (T= 0:+10) (Part 2)

Event 7 8 9 10 11 12

Cumulated Average

Abnormal Returns of

companies on event period -0,86% 0,68% -0,52% 0,47% 0,86% 0,34%

CAR (0:+10)

ROYAL DUTCH SHELL

PLC 3,22% -5,26% 1,13% -0,80% -2,52% -3,41%

BP PLC 3,29% -2,70% 1,76% -1,74% -3,24% 2,04%

HSBC HOLDINGS PLC -0,63% 1,13% -2,68% 1,83% 1,94% -0,26%

LLOYDS BANKING

GROUP PLC -4,80% 3,87% -1,56% -2,83% -0,02% -6,90%

PRUDENTIAL PLC -5,45% 2,95% -0,44% 3,57% 1,90% -7,55%

BARCLAYS PLC -3,71% 3,43% -5,80% 0,28% 4,66% -2,59%

ROYAL BANK OF

SCOTLAND GROUP PLC -5,67% 5,10% -4,39% -7,41% 4,19% -13,16%

STANDARD CHARTERED

PLC 0,30% 3,35% -6,67% 5,01% 3,77% -3,01%

LEGAL & GENERAL

GROUP PLC -1,18% 5,68% 0,40% -0,61% 3,41% -5,36%

ASTRAZENECA PLC 0,65% -3,60% 0,97% -5,09% -1,88% 13,84%

GLAXOSMITHKLINE PLC -3,42% -3,29% 2,59% 1,45% 0,65% 3,47%

BRITISH AMERICAN

TOBACCO PLC 4,26% 2,92% -0,74% -3,22% 7,86% 3,25%

DIAGEO PLC 2,51% -4,98% 2,12% 3,60% -0,96% 3,16%

UNILEVER PLC 4,03% -0,94% 4,53% 6,55% 0,94% -0,42%

RECKITT BENCKISER

GROUP PLC -2,87% -6,58% 3,72% 4,88% -0,97% -5,53%

TESCO PLC 0,14% 1,53% -0,57% 0,92% 0,03% -3,03%

ASSOCIATED BRITISH

FOODS PLC -

10,18% 7,36% 4,50% 1,68% -1,95% 2,70%

IMPERIAL BRANDS PLC 2,23% 1,02% -1,93% -1,78% 7,04% 1,06%

EXPERIAN PLC 2,51% 2,91% -1,09% 1,08% -4,82% 5,18%

CRH PLC -4,26% 1,15% -5,42% 1,94% 1,99% 3,01%

BAE SYSTEMS PLC -8,51% 2,05% -4,67% -2,91% -1,37% 7,34%

RIO TINTO PLC 2,70% 5,26% 2,03% -6,75% -1,61% -5,56%

VODAFONE GROUP PLC 1,86% -8,07% 2,34% 2,11% 4,95% 15,63%

RELX PLC -0,15% 0,79% -3,28% 7,40% -2,16% 1,54%

NATIONAL GRID PLC 1,53% 2,00% 0,09% 2,66% -0,38% 3,21%

84

Individual Companies Cumulated Abnormal Returns of period (T= 0:+10) (Part 3)

Event 13 14 15 16 17

Cumulated Average

Abnormal Returns of

companies on event period 0,52% 0,35% -0,72% 0,10% -0,35%

CAR (0:+10)

ROYAL DUTCH SHELL

PLC 2,02% -3,19% -0,43% -2,59% -6,00%

BP PLC 1,62% -2,08% 0,38% -3,83% -2,74%

HSBC HOLDINGS PLC 1,10% -0,28% -4,03% -0,54% -3,44%

LLOYDS BANKING

GROUP PLC 7,51% 16,62% -7,99% -0,94% -0,24%

PRUDENTIAL PLC 4,26% -10,61% -2,61% -1,09% 6,18%

BARCLAYS PLC 8,40% 13,23% -0,51% 1,23% 2,79%

ROYAL BANK OF

SCOTLAND GROUP PLC 11,42% 16,75% -12,98% 1,33% -2,41%

STANDARD CHARTERED

PLC 6,12% 0,52% 4,80% -4,78% -3,19%

LEGAL & GENERAL

GROUP PLC 11,35% -1,65% -3,61% 5,51% 0,64%

ASTRAZENECA PLC -6,90% -2,31% 7,94% 1,35% 0,15%

GLAXOSMITHKLINE PLC -5,95% -2,68% 4,31% -0,55% -7,39%

BRITISH AMERICAN

TOBACCO PLC 1,71% -8,01% 2,40% 4,37% -0,07%

DIAGEO PLC -10,22% -2,46% -1,20% 1,40% 0,38%

UNILEVER PLC -7,57% -4,41% -1,25% -6,62% 1,15%

RECKITT BENCKISER

GROUP PLC -3,42% -1,95% -1,70% -0,40% -0,06%

TESCO PLC 3,10% 0,64% -4,53% 2,68% 0,76%

ASSOCIATED BRITISH

FOODS PLC -2,15% 1,87% 2,60% 3,69% -0,19%

IMPERIAL BRANDS PLC 1,23% 1,00% -8,90% 7,53% -3,57%

EXPERIAN PLC -5,25% -4,04% -0,42% -0,38% 2,60%

CRH PLC -2,35% 1,58% 1,66% -3,44% 1,32%

BAE SYSTEMS PLC 5,31% -0,04% 5,94% -4,37% 3,71%

RIO TINTO PLC 0,95% -2,66% 3,08% -2,43% -0,40%

VODAFONE GROUP PLC -0,11% 3,38% -1,63% -0,39% 0,83%

RELX PLC -6,89% -3,99% 2,30% 0,96% 0,51%

NATIONAL GRID PLC -2,18% 3,50% -1,57% 4,74% 0,05%

85

Annex 8

Various Sectors Cumulated Abnormal Returns of all periods

CAAR (-10:10)

CAAR

(-5:5)

CAAR

(-2:2)

CAAR

(-10:0)

CAAR

(0:10)

Energy Sector -4,07% -1,43% -1,33% -1,52% -2,55%

Financial Sector 11,04% 9,75% 9,11% 10,71% 0,34%

Health-Care sector -1,49% -2,03% -0,55% -1,74% 0,25%

Consumer Staples

Sector -4,39% -3,01% -3,23% -4,46% 0,08%

Industrial Sector 1,10% 0,93% -0,23% 1,43% -0,33%

Basic Resources -1,07% -1,34% -0,84% -0,61% -0,45%

Telecommunications 1,90% 1,60% 0,97% 1,01% 0,88%

Consumer

Discretionary -0,90% -0,35% -0,59% -0,97% 0,07%

Utilities 1,49% 0,27% -0,17% 1,06% 0,44%