Comparative Market Efficiency: A comparison of the efficiency of the Dutch and Italian stock markets

8/9/2019 Comparative Market Efficiency: A comparison of the efficiency of the Dutch and Italian stock markets

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Utrecht University

Utrecht School of Economics

Bachelor Thesis

COMPARATIVE MARKET EFFICIENCY

A comparison of the efficiency of the Dutch and Italian stock markets

Authors: Supervisor:

Erik P. van der Heijden Luigi F. Pinna

Ellen W.H. Klijnstra

Group supervisor:

Peter O. van der Meer

ABSTRACT:In this thesis we test if one stock market can be more efficient than another. We

analyze the Dutch and Italian stock markets and compare them in terms of efficiency. A test is

conducted for the weak-form of efficiency and the results are interpreted. We find that the

Italian stock market is relatively more efficient than the Dutch stock market due to differences

in the degree of information disclosure, market composition, and historical and cultural

development of the two stock markets.

KEYWORDS: Efficient Market Hypothesis, Stock market, Information disclosure, Abnormal

returns

This bachelor thesis is composed to fulfill the graduation requirements for the

Bachelor Economics & Business Economics at Utrecht School of Economics.

24thof June, 2014


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Acknowledgements

We would like to thank Luigi F. Pinna MSc, our thesis supervisor, for his support,

guidance, suggestions, feedback and help during our process of doing research and

writing this thesis. We would also like to thank drs. Peter O. van der Meer, our

group supervisor, for his time, feedback and insightful group meetings.

Furthermore, we would like to express our appreciation to our fellow students for

their peer-review and constructive criticism.


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Table of Contents

List of Figures.................................................................................................................................................... iv

List of Tables....................................................................................................................................................... v

List of Equations................................................................................................................................................ vi

1.0 Introduction............................................................................................................................................ 1

2.0 Theoretical Background............................................................................................................................. 2

2.1 What Is Market Efficiency........................................................................................................ 2

2.2 Development Of The Efficient Market Hypothesis................................................................... 3

2.3 Why Compare Netherlands and Italy........................................................................................ 52.4 Evolution Of The Dutch and Italian Stock Market.................................................................... 6

2.5 Mechanism Of The AEX and FTSE MIB................................................................................. 8

3.0 Data Description....................................................................................................................................... 11

3.1 Data......................................................................................................................................... 11

3.2 Portfolio NL............................................................................................................................. 13

3.3 Portfolio IT.............................................................................................................................. 14

4.0 Empirical Research.................................................................................................................................. 15

4.1 Methodology............................................................................................................................ 15

4.2 Testing..................................................................................................................................... 16

4.3 Results..................................................................................................................................... 21

4.3.1 Results Of Portfolio NL........................................................................................................ 21

4.3.2 Results Of Portfolio IT......................................................................................................... 22

4.3.3 Comparative Results Of Portfolio NL and Portfolio IT....................................................... 23

5.0 Interpretation Of Results................................................................................................................... 24

5.1 Mechanism Of AEX and FTSE MIB...................................................................................... 24

5.2 Composition Of Portfolio By Industry.................................................................................... 25

5.3 Information Disclosure and Transparency............................................................................... 27

5.4 Cross-Listings and Time Zones............................................................................................... 28

5.5 Cultural Differences................................................................................................................ 29

6.0 Conclusion and Recommendation.................................................................................................... 31

Bibliography..................................................................................................................................................... 32


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List of Figures

Figure 1: Graphical representation of regression of historical returns of Aegon vis--vis

historical returns of AEX

Figure 2:Normal Distribution of Portfolio NL

Figure 3:Normal Distribution of Portfolio IT

Figure 4:Degree of information disclosure in the Netherlands and Italy

Figure 5:Percentage of individuals in the Netherlands, European Union, and Italy who use

internet banking from 2005 through 2013


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List of Tables

Table 1:Example of historical prices and return

Table 2:Composition of the Dutch portfolio

Table 3:Composition of the Italian portfolio

Table 4: Regression of historical returns of Aegon vis--vis historical returns of AEX

Table 5:Cumulative abnormal returns for Portfolio NL ranked from smallest to largest

Table 6: Cumulative abnormal returns for Portfolio IT ranked from smallest to largest

Table 7:Mean and standard deviation of Portfolio NL and Portfolio IT

Table 8:Rules for selection

Table 9: Industry division of Portfolio NL and Portfolio IT

Table 10: Time zones in which Portfolio NL and Portfolio IT are present in, indicated with the

number of companies


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List of Equations

Equation (1): Index value of the AEX

,= ,,,,

Equation (2.0): Index value of FTSE MIB

It= Mt/ Dt

Equation (2.1): Total free float adjusted market capitalization

Equation (2.2): Investable Weighting Factor

IWF = 100% - sum of the % of shareholders held by restricted shareholders

Equation (3): Capital Asset Pricing Model

+ + Equation (4): Trend line

0.0016269 + 1.614483Equation (5): Abnormal returns

AR = Y

Equation (6): Abnormal returns extended

AR = Y( + irm)


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1.0Introduction

It goes without question that capital markets are integral to the monetary wealth of the modern

Western world. From the inception of the first limited-liability joint stock company in the

seventeenth century to the electronic trading floors of the twenty-first century, the buying and

selling of securities has been of great importance to the flow of capital in and out of numerous

countries. Despite the importance of these markets in 2014, their innate nature is still under

debate. Perhaps one of the largest contributions to the understanding of capital markets is the

Efficient Market Hypothesis which loosely states that stock prices at any given time reflect all

relevant information. While a number of economists agree with the basis of this hypothesized

nature of capital markets, there is empirical evidence indicating that not all capital markets are

perfectly efficient at all times. Taking into consideration that a stock market is nearly

perfectly efficient, but does not indeed reach efficiency perfection, we ask about the

comparative nature of stock markets. It is the intention of this thesis to investigate if one stock

market can be relatively more efficient than another.

We conduct our research by comparing the market efficiency in the weak form of the Dutch

stock market and the Italian stock market. Our investigation is structured in the following

manner. Section 2 discusses the theoretical background of our research question. Section 3

defines the data used to conduct our analysis. Section 4 describes the methodology used to

conduct our empirical analysis which is followed by our empirical test and results. Section 5

interprets the results of our empirical analysis according to different factors. Finally, Section 6

concludes this thesis.


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2.0 Theoretical Background

This section lays the foundation for the theoretical background of the Efficient Market

Hypothesis and how it is used in our thesis. In Section 2.1 we discuss what exactly the Efficient

Market Hypothesis is. In Section 2.2 we talk about how this theory has developed over time. In

Section 2.3 we justify the comparison of the Netherlands to Italy. In Section 2.4 we compare

how the Dutch and Italian stock markets have been constructed from their beginnings to 2014.

In Section 2.5 we analyze the inner workings of the index representative of the Dutch stock

market, the AEX, vis--vis the representative Italian stock market index, the FTSE MIB.

2.1 What Is Market Efficiency?

If investors had the ability to predict the future intrinsic value of a security, they would be able

to earn unprecedented profits from even the slightest price movement. The price of a security

moves upward or downward in response to new information, or news, which by definition is

unpredictable. This implies that the movement of stock prices themselves is unpredictable.

Kendall and Hill (1953, p. 11) reiterate this by stating that, An analysis of stock-exchange

movements revealedthere is no hope of being able to predict movements on the exchange.

This stock price unpredictability is encompassed in the concept of the random walk which

requires that the expected value of a stock movement is zero due to the equal probability that it

may move up or down. If prices are determined rationally, then only new information will

cause them to changeA random walk would be the natural result of prices that always reflect

all current knowledge (Bodie, Kane, & Marcus,2011, p. 372).

Empirical evidence from economists such as Kendall and Hill (1953), Cootner (1964), and

Fama (1965) leads to the observation that stock prices reflect available and relevant information

and that they are unpredictable. This characteristic of securities markets is known as theEfficient Market Hypothesis. From its inception in the twentieth century through its

development into the twenty-first century, the Efficient Market Hypothesis has been the main

source of explanation for the valuation and movement of stock prices in securities markets. This

hypothesis exists in three relevant forms which attempt to explain market efficiency through

three varying perspectives on what qualifies as all available and relevant information.

The weak-form of efficiency takes into account the historical prices of a security. The weak-

form hypothesis asserts that stock prices already reflect all information that can be derived by


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examining market trading data such as the history of past prices, trading volume, or short

interest (Bodie, Kane, & Marcus, 2011, p. 375). From this form of efficiency, it can be implied

that any analysis on price trends will result in zero profits. If a profitable trend is spotted, it will

be exploited by all other investors who, theoretically, have access to the same historical price

information which is virtually costless. An arbitrage opportunity that is exploited will drive the

price of a security to its intrinsic value with buy or sell signals in accordance to the securitys

supply and demand. In a rational market, a security whose market value is equal to its intrinsic

value cannot be used to earn profit because its price will not deviate from its current and

accurate position

The semi-strong form of efficiency takes into account all public information about the firm thatis issuing a security. Such information includes, in addition to past prices, fundamental data

on the firms product line, quality of management, balance sheet composition, patents held,

earning forecasts, and accounting practices (Bodie, Kane, & Marcus, 2011, p. 376). Public

news on factors such as these which can affect the profitability of a firm, and thus the value of

its stock, is by definition unpredictable. According to the semi-strong form of efficiency, this

firm-specific information is reflected in stock prices. The unpredictability of this information

eliminates guaranteed opportunities to profit on speculation.

The strong-form of efficiency takes into account all private information about the firm that is

issuing a security. This form also includes public information and historical prices. The strong-

form of the efficient market hypothesis states that stock prices reflect all information relevant

to the firm, even including information available only to company insiders (Bodie, Kane, &

Marcus, 2011, p. 376).

2.2 Development Of The Efficient Market Hypothesis

The conceptualization of the Efficient Market Hypothesis dates back as far as the turn of the

twentieth century, but throughout its history it did not always have the same title. In his 1900

work, Louis Bachelier identifies that events from the past, the present, and discounted for the

future are reflected in the price of an asset but they are not apparently correlated to price changes

(Cootner, 1964). This empirical finding was discovered in a time when economists did not have

regular access to computer technology to easily run calculations for lengthy sets of data.

Decades later, Kendall and Hill (1953) used more advanced methods of computation and built

upon this previous study on historical price autocorrelation. In their publication, they enquirewhether the so-called trend in a series was in fact separable from the short-term movements, or


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whether it should be regarded as generated by a set of forces which also gave rise to the short-

term movements (Kendall & Hill, 1953, p. 11). Their findings indicate that movements in a

stock market show noticeably little serial correlation and little lag correlation among series.

Up to the mid-twentieth century, the observation that security prices have no significant,

statistical relationship with their past prices was tested by numerous economists. It was not until

the critically acclaimed work by Fama (1965), that markets were defined as efficient for the

first time. In later works, Fama (1970) further elaborates on stock market behavior. He argues

that security prices at any time fully reflect available information, and are thus efficient. Fama

(1991) continues to do a review and extension on his work of 1970. In this paper he analyzes

the efficient market hypothesis according to the following factors: event studies, private

information and return predictability. His findings on the topic of event studies are that stock

prices adjust quickly to information. Concerning private information, he states that insider

trading now plays a major role in testing for market efficiency, and he touches upon the topics

of portfolio management and security analysis which have become more important variables in

measuring the extent to which private information is available. The results of his study on return

predictability unveils that looking at the long-horizon stock returns, predictability from past

returns are not possible and that there is no autocorrelation. However, he finds that based on

other variables, such as price to earnings ratios, dividend yields and default spreads of low-

over high-grade bond yields, it is possible to predict returns.

While theories on market efficiency throughout the twentieth century are strongly developed,

not all economists agree, and offer counterevidence to the argument. Basu (1977) questions the

validity of the Efficient Market Hypothesis by showing empirical evidence of the systematic

bias of particular securities. The author concludes that from April 1957 through March 1971,

stock portfolios with relatively low price-to-earnings ratios display on average higher absolute

and risk-adjusted rates of return than high price-to-earnings portfolios. The fact that a bias of

any kind of security exists is a contradiction to the Efficient Market Hypothesis. Tversky and

Kahneman (1986) continue to counter argue the Efficient Market Hypothesis by questioning

the assumptions that investors are rational. Rationality of actors in the stock market is integral

to the Hypothesis because it makes the demand and pricing of securities unbiased by erratic

decision making. Pertaining to human behavior deviating from rational behavior, Tversky and

Kahneman (1986) state that, The deviations of actual behavior from the normative model aretoo widespread to be ignored, too systematic to be dismissed as random error, and too


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fundamental to be accommodated by relaxing the normative system. Laffont & Maskin (1990)

argue that imperfect information on capital markets can lead to a violation of the strong form

of the Efficient Market Hypothesis. By releasing limited, select information, or by otherwise

conducting insider trading, investors on the stock market can misconstrue the real price of an

asset, thus going against the main principle that a price reflects all relevant information. The

Efficient Market Hypothesis makes the assumption that all actors in a market behave within

their legal parameters, but crimes such as insider trading still exist. Maikel (2003) also states

his disagreement with the Efficient Market Hypothesis. He argues that investors are irrational

and that pricing irregularities and patterns can persist over given periods of time. He does not

condone the absolute abandonment of the Efficient Market Hypothesis, but he does claim that

it can be further sophisticated.

Throughout its development, the Efficient Market Hypothesis has been both strengthened and

countered by numerous economists. Vast amounts of research have gone into the subject, but

there is still disagreement in the academic community. Even the Nobel Prize in Economic

Sciences was given to economists that have opposing perspectives on the same concept. The

press release from the Royal Swedish Academy of Sciences, in awarding the Sverges Riksbank

Prize in Economic Sciences in Memory of Alfred Nobel for their empirical analysis of asset

prices states that, These findings, which might seem both surprising and contradictory, were

made and analyzed by this years Laureates, Eugene Fama, Lars Peter Hansen and Robert

Shiller (The Royal Swedish Academy of Sciences, 2013). If even these Nobel laureates cannot

agree on the validity of the Efficient Market Hypothesis, then there is still room for more

development.

2.3 Why Compare Netherlands and Italy

The Efficient Market Hypothesis has a strong degree of legitimacy, but it is not universally

agreed upon. Based on the assumption that markets can display efficiency, but are not

necessarily perfectly efficient, present economic theory would indicate that one stock market

can be more efficient than another stock market. Bodie, Kane and Marcus (2011) state that, It

would not be surprising to find that the degree of efficiency differs across various markets.In

order to test this notion in a controlled experiment, we use the stock markets of the Netherlands

and of Italy. The Netherlands and Italy use the same currency, are both currently in the same

European Union, and are both in the same time zone. The two nations have developed Western-

style capital markets which were established hundreds of years ago, and thus have both


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developed parallel to one another. The stock markets of the Netherlands and of Italy display

many similar characteristics, thus we would expect them to display the same degree of market

efficiency.

2.4 Evolution Of The Dutch and Italian Stock Market

In a modern age of quickly-growing globalization and national economic interdependence,

capital flows within and between countries have become highly relevant. A capital market is an

integral component to most nations wealth and has become more so since their creation in the

early seventeenth century. Still, economists in the twenty-first century have difficulty agreeing

universally on this type of markets behavior and its innate characteristics. Although both the

Dutch and Italian stock markets in 2014 are well established and internationally oriented, they

have different histories of development.

The origins of the Dutch stock market go back to 1602 when the Vereenigde Oostindische

Compagnie (VOC) was founded. It was the first large limited-liability joint stock company in

the world and was in many respects seen as the first ever initial public offering. According to

de Jong & Rell (2005), By the middle of the seventeenth century the Netherlands had

developed an active shareholding culture, with speculation in VOC shares and even derivatives

trading a widespread popular pursuit. The Golden Age was a flourishing era for the Dutch

stock market, where wealth was also invested on an international level in government securities.

As de Jong & Rell (2005) state in the words of Peter Hgfeldt, The free city of Amsterdam

provided the fertile ground for the first modern hub of international financial markets and

advanced intermediation. In 1799, when the VOC declared bankruptcy, wealth and confidence

in the Dutch stock market diminished. This decline in stock market liquidity was also due to

French occupation of the Netherlands between 1795 and 1813. The Netherlands was lagging

behind in the industrial revolution comparing to its surrounding countries, especially the UK.A period of reduced economic growth and stagnation followed and went along with low activity

on the capital market. Finally when the industrial revolution got off the ground in the

Netherlands in the late nineteenth century, there was a revival in the Dutch stock market.

Industrial development started coming to life in the second half of the nineteenth century, with

new shareholder capital raised for a number of enterprises such as railway construction (de

Jong & Rell, 2005, p. 469). The main actors in the capital market were members of the

founding families and wealthy individuals who brought in the capital.


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Throughout the nineteenth century the Amsterdam Stock Exchange was an active and

sophisticated market. It brought large savings to the market and its open and competitive

character led to an increase in unrestricted public access. However, it was still the case that the

trading elite of Amsterdam played a major role in financing and the industrialization. In the

early twentieth century the Amsterdam Stock Exchange established a physical trading floor

called the Beursplein 5. During this time, Beursplein 5 grew to be the financial center of the

Netherlands, and functioned as a trading floor for 85 years. In 1983 the Amsterdam Exchange

indeX, AEX, was introduced and functioned since then as the main index of the Amsterdam

Stock Exchange.

In the beginning of the twentyfirst century, the Amsterdam Stock Exchange entered a new era

after 400 years of business. The trading floor became automated and transactions were present

in international capital markets through digitalization and world wide acces (Euronext, 2014).

In the year 2000, the AEX, together with the UK, Portugal, France and Belgium, created the

first pan-European exchange called the Euronext. The Euronext became a stock exchange

which consisted of several of the largest indices of each its particiating countries. Its purpose

was to be an internationally oriented stock exchange and to benefit from the harmonization of

the European Union with respect to financial markets. In 2007, the Euronext merged with the

New York Stock Exchange Group, forming Euronext NYSE which was the first global stock

exchange.

By the time the Dutch stock exchange was already established as a progressive pioneer, the

Italian stock market had only come into development in 1808. With the help of Eugenio

Napoleone the Milan Securites Exchange was inaugurated. Unlike the free-market Anglo-

Saxon system of stock exchange, the Milan Securities Exchange was created by the government

and subject to the Napoleonic Code of Trade until 1998 (Paletta, 2007). The stock exchange in

Milan, similar to Rome, Turin and Genoa, was one of many independent, local exchanges in

Italy during its time of political fractionalization before 1946. Aganin & Volpin (2005, p. 334)

state that, The absence of regulation offered speculators wide opportunities to profit and keptuninformed investors (and liquidity) away. The turning point for the MSE came with the intense

industrialization push between 1895 and 1907. The industrial revolution began quite late in

Italy, but boosted the Milan Stock Exchange. The number of traded companies increased, banks

became more active and the government showed a direct presence in the Italian economy.


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In 1918, this period of growth and prospertity slowed down and the Milan Stock Exchange

became Italys main stock exchange. Aganin & Volpin (2005, p. 336) describe this turning

point by stating that, One important cause of the end of the period of growth for the stock

market was the lack of protection for minority shareholders. There was a general market

perception that universal banks and managers like Agnelli used the investment boom early in

the century to pump and dump their shares. Further, the drastic increase of dividend taxation at

company and personal level introduced by the fascist government made investment in the stock

market even less attractive. In the twentieth century the stock exchange was marked by first

privatizations of companies and industries, liberazation of capital markets. It also began to

witness low legal enforcement, low legal investor protection, high ownership concentration and

family capitalism which were strongly conncected to political power. Underdeveloped

institutional characteristics and the presence of politics in the financial market made the capital

market an underdeveloped one and the general public shied away from entering. Another issue

was the increase of public debt. This debt was due to state-owned enterprises who

malfunctioned in terms of inefficient production technologies, misallocation of resources and

weak managerial incentives, to name a few. These losses were financed by the government with

public debt. In the 1990s, when the public debt drifted out of control, a privatization program

was introduced (Aganin & Volpin, 2005). In 1998, the Milan Stock Exchange was privitzed

and was thus titled the Borsa Italiana.Since 1998, the Borsa Italiana has converged into a well-

developed and international oriented stock market that is on its way to position itself strongly

on the competitive global market. In 2007, the Borsa Italiana and the London Stock Exchange

merged and formed the London Stock Exchange Group. The purpose of the creation of London

Stock Exchange Group is to form a well-diversivied European exchange group with a core

function of bringing together companies who seek capital with investors from all over the

world. Two years later, in 2009, the index FTSE MIB was created, which was before managed

by the the S&P MIB, and functioned since that time as main index for the Italian stock market.

2.5 Mechanism Of The AEX and FTSE MIB

In order to measure the Dutch and Italian stock exchange, we use indices as proxies. An index

has specific working and rules of composition and operating, thus we discuss both of the indices

use in our empirical testing: the AEX to represent the Netherlands and the FTSE MIB to

represent Italy.


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The AEX consists of the 25 largest weighted companies listed on the Amsterdam Stock

Exchange in terms free float market capitalization ranking. Free float market capitalization is

the outstanding capital of a company that should be freely available for trading. Shares held by

insiders of a company, government holdings and holdings of particular companies are not

considered as free float. Also shares held by pension funds and mutual funds fall outside free

float (NYSE Euronext, 2014). Market capitalization is the total market value of a companys

outstanding shares. Hence, a company is eligible for entering the AEX index when its free float

is 15%, meaning that 15% of its outstanding shares should be freely available for trading.

Additionally, the free float velocity should be 25%, meaning that their actual trading volume

should be at least 25% of the total shares listed for trading. The capping of the AEX is 15%,

which means that no single company of the index can comprise more than 15% of the index.

The index is based on a price return basis of the companies, which is based on equation 1.

, ,,,,

(1)

Where the variables denote the following; t is the time of calculation, N is the number of

constituent equities in index, Qi,t is the number of shares of equity i included in the index on

day t,Fi,t is the free float factor of equity i,fi,t is the capping factor of equity i, Ci,t is the price

of equity i on t,Xi,t is the current exchange rate on t and dtis the divisor of the index on day t.

We have chosen for the AEX to represent the Netherlands because this is the largest index of

Dutch indices and is representative of the Dutch equity markets and economy. The AEX

consists of the largest 25 companies listed on the Amsterdam Stock Exchange, the AMX is next

with the second 25 largest listed companies, and the AScX represents the shares of the third

largest 25 companies (NYSE Euronext, 2014). Although the AEX index consists of 33% of all

listed Dutch companies, since these are the largest companies its market capitalization is 55%

of the domestic market capitalization (tradimo Ltd., 2014)[1]. Since data on marketcapitalization is not regularly available for the Dutch stock market, we have based this number

on data available in the year 2011.

The FTSE MIB index is comprised of the 40 most liquid companies listed on the Borsa Italiana.

The index is the primary benchmark index for the Italian equity markets, capturing

approximately 80% of the domestic market capitalization (London Stock Exchange Group,

2013, p. 3). The weighting factor of the FTSE MIB is the same as the AEX in that is based on

free float market capitalization and the index is also capped at 15%. For entering the index,


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companies must fulfill the requirement of a free float percentage of at least 15%, but no rules

are set out for free float velocity as the AEX does.

Furthermore, the FTSE MIB is based on a price return basis of the companies which is follows

Equation 2.0. This is the same equation for the FTSE MIB as for the AEX, but different

notations are used.

It= Mt/ Dt (2.0)

Dtis the value of the index divisor at time t.

Where Mt = total free float adjusted market capitalization at time t, which equals:

(2.1)

Where the variables denote the following;pitis the last traded price at time t, of the ithshare,

qitis the number of shares in the index.

IWFitis the Investable Weighting Factor (adjusted for capping) for the ithshare:

IWF = 100% - sum of the % of shareholders held by restricted shareholders (2.2)

We have chosen for the FTSE MIB since this index is the primary index for the Italian stock

market, representing, as mentioned before, 80% of the Italian stock market, what we consider

as a significant sample.

Chapter 5 discusses more features of both indices, in order to explain differences and

similarities of the AEX and FTSE MIB. At this point, an understanding of these basic concepts

about composition, weighting factors and the general index equation is enough to have

knowledge about the mechanisms of the indices.


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3.0 Data Description

This section discusses the data we use for our empirical test. Section 3.1 explains the data we

need for our regressions and calculations for abnormal returns, where section 3.2 and 3.3

present the Dutch and Italian Portfolio respectively.

3.1 Data

For our comparative study, we test and analyze the Dutch and Italian stock markets. In order

to make this measurable, we make portfolios for each country and use them as proxies for

their stock markets. These portfolios can be seen as a sample from which we infer results for

the population. We choose to use the most representative index of each country, respectively

the AEX index for the Netherlands and the FTSE MIB for Italy. The companies in these

indices are taken into account for our research. For an overview of the portfolios, see Sections

4.2 and 4.3.

For our empirical research, testing for the weak form of efficiency is done by testing for

abnormal returns. An abnormal return is the difference between a stocks actual return and its

expected return. For the actual return we gather data from Yahoo Finance which consists of

historical prices on a daily basis. Our research is based on the daily stock returns of all

companies listed in both indices, 65 companies, from 4 January 2000 through 23 April 2014.

A stock return in our research is considered to be the percentage change in a stocks market

price from one day to the next. The time span of our data begins on the second trading day of

the year 2000 in order to begin with a meaningful stock return for that year. We take the year

2000 as a starting point since in 1998 the Italian stock market privatized and there is a lack of

data in the years 1998 and 1999. Data for our selected companies are regularly available on

http://finance.yahoo.com.

With the daily historical prices given, we are able to calculate the actual stock return obtained

each day. We calculate the daily return for the index as well as for each individual company of

the index. The reason we do this is that we want to have data as well for the index, to use this

as a benchmark and be able to compare the performance of an individual company to its index.

As table 1 illustrates, the daily actual returns are calculated for the Dutch index, the AEX, and

for an individual company in this portfolio, Aegon. This method is applied to all companies in

both portfolios for every day between 2000 and 2014. An important note on a flaw of our data
http://finance.yahoo.com/http://finance.yahoo.com/


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is that not all stock prices, and thus not all stock returns, are available for all companies. Gaps

exist in our data due to the fact that some companies have released more price information than

others. The maximum number of observations of stock returns for any given company in our

research is 3678.

Table 1: Example of historical prices and return

Date AEX (AEX) Aegon

(AGN.AS)

Adjusted

closing price

Change in

adjusted closing

price

Adjusted

closing price

Change in

adjusted closing

price

04-01-2000 642.25 3.92

05-01-2000 632.31 -0.015476839 3.77 -0.038265306

Source: Yahoo Finance

With these closing prices and returns we are able to make further calculations and regressions

in order to calculate the expected return. This is explained in more depth in Section 4.1 and

4.2. The described literature of Section 2.2 is applied to set the foundations of our research.

Our empirical testing is based on our data which is a time series. Data output that is not given

in the associated section is available on request.


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3.2 Portfolio NL

In this section the composition of the Dutch Portfolio is given. As a recap, the AEX consists of

the 25 largest companies listed on the Amsterdam Stock Exchange as of April 2014. They are

presented in an alphabetical order.

Table 2: Composition of the Dutch portfolio

Companies listed in the AEX

Dutch stock market (25 companies)

1.) Aegon

2.) Ahold

3.) Akzo Nobel

4.) ArcelorMittal

5.) ASML

6.) Boskalis

7.) Corio

8.) Delta Lloyd

9.) DSM

10.) Fugro

11.) Gemalto

12.) Heineken

13.) ING Group

14.) KPN

15.) Oci

16.) Philips

17.) Randstad Holding

18.) Reed Elsevier

19.) Royal Dutch Shell

20.) TNT Express

21.) SBM Offshore

22.) Unibail-Rodamco

23.) Unilever

24.) Wolters Kluwer

25.) Ziggo

Source: AEX (2014)


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3.3 Portfolio IT

The composition of the FSTE MIB is presented in this section. All companies are listed in an

alphabetical order as they are present in the index in April 2014.

Table 3: Composition of the Italian portfolio

Companies listed in the FTSE-MIB

Italian stock market (40 companies)

1.) A2A

2.) Atlantia

3.) Autogrill

4.) Azimut Holding

5.) Banca Monte dei Paschi di Siena

6.) Banca Popolare dell'Emilia

Romagna

7.) Banca Popolare di Milano

8.) Banca Popolare

9.) Buzzi Unicem

10.) Campari

11.) CNH Industrial

12.) Enel

13.) Enel Green Power

14.) Eni

15.) Exor

16.) Fiat

17.) Finmeccanica

18.) Generali

19.) GTECH

20.) Intesa Sanpaolo

21.) Luxottica

22.) Mediaset

23.) Mediobanca

24.) Mediolanum

25.) Moncler

26.) Pirelli & C

27.) Prysmian

28.) Saipem

29.) Salvatore Ferragamo

30.) Snam

31.) STMicroelectronics

32.) Telecom Italia

33.) Tenaris

34.) Terna

35.) Tod's

36.) UBI Banca

37.) UniCredit

38.) Unipolsai

39.) World Duty Free

40.) YOOX

Source: Borsa Italiana (2014)


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4.0 Empirical Research

This section describes the process of our empirical research. Section 4.1 explains the

methodology we use conduct our experiment. Section 4.2 walks through the mechanics of the

empirical test. Section 4.3 discusses the results of the empirical test.

4.1 Methodology

In a perfectly efficient and rational stock market which is subject to the weak-form of the

Efficient Market Hypothesis, all returns of all companies are expected (i.e. normal) returns. The

first step of our empirical testing procedure is to determine if one portfolio has a greater amount

of unexpected (i.e. abnormal) returns than another, and then to make further conclusions based

on these values. Prevailing economic theory indicates there should be no difference inefficiency between two stock markets. We thus come to the following null and alternative

hypotheses:

H0: There is no difference between the weak-form efficiency of two stock markets.

HA: There is a difference between the weak-form efficiency of two stock markets.

To test for abnormal returns, we begin with the Capital Asset Pricing Model, also known as

CAPM. The CAPM is a set of predictions concerning equilibrium expected returns on riskyassets (Bodie, Kane, & Marcus, 2011, p. 308). With this model, it is possible to formulate an

educated prediction on the expected return of an asset given the risk-free rate of treasury bills,

the assets degree of sensitivity to macroeconomic conditions (i.e. beta-coefficient), and the

return of the assets market. With these variables, the following equation can be made:

+ + (3)

In Equation (3), ri, is the expected return of asset i. The variable, ,is the rate of return of asset

i when the market return is equal to zero. The coefficient,, is the degree of sensitivity of asset

i on its respective market. The variable, rm, is the return of the market. The variable, ei,is the

error term.

In our empirical research, we neglect the risk-free rate of treasury-bills in the Netherlands and

in Italy so that we may conduct an experiment void of variables that can misconstrue our

analysis of securities in our two portfolios. By regressing firm-specific returns on their market

returns, we find the expected return of a stock. Any discrepancy between a stocks actual return

and its expected return is an abnormal return. An abnormal return in this scenario is an


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indication of weak-form market inefficiency. By adding all abnormal returns for all components

of both portfolios, we obtain each companys cumulative abnormal return. We take the

cumulative abnormal returns of each company and add them together with the cumulative

abnormal returns of all other companies in the same portfolio. The two portfolios are either

Portfolio NL which represents the Dutch stock market, or Portfolio IT which represents the

Italian stock market. If we find that one portfolio has a higher percentage of companies with

abnormal returns that lie outside of its respective portfolio standard deviation of cumulative

abnormal returns, we can determine that this portfolio is relatively less efficient, and thus this

stock market is relatively less efficient. We do not comment on the objective efficiency of both

stock markets, we only display the relative observed efficiency of both stock markets from the

perspective of an investor.

Our first step is to calculate the abnormal return for every company on all days in our dataset.

Since the actual return is already recorded in our data, we only need to calculate the expected

return. The expected return is obtained from first running a regression of a company on its

market and to use the specific output of the regression to create a market trend line. This market

trend line represents the normal returns. If a market is fully efficient, all company returns should

lie exactly on this line. However, we expect that the returns of the companies vary around the

trend line. Using the specific values for alpha and beta from the regression, we can calculate

the expected return and subtract this from the actual return.

With the values of cumulative abnormal returns and standard deviations of all our companies

and portfolios, we are able to determine which companies are outliers. We consider a company

an outlier when the absolute value of its cumulative abnormal return is larger than the absolute

value of the standard deviation of the index.

Using the processed data on which companies are relative outliers within their respective

portfolios, we are able to create normal distributions of the cumulative abnormal returns. Using

a normal distribution for both portfolios allows for the calculation of the probability of a specific

value of cumulative abnormal returns with respect to the mean of the portfolio. It also makes

the identification of companies that lie outside of standard deviation easier to identify and

visualize.

4.2 Testing

Our empirical testing begins by running a regression of all available historical stock returns of

a given company on the historical returns of its respective market index. We utilize the null


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hypothesis that the percentage of companies that lies outside of standard deviation of

cumulative abnormal returns is the same for both portfolios. Using the variable XNLto represent

the number of outliers in the portfolio PNL, Portfolio NL, and the variable XITto represent the

number of outliers in portfolio PIT, Portfolio IT, we come to the following null and alternative

hypotheses:

:

:

We run a regression of an individual company on its market index to see if the company moves

in the same direction and with the same magnitude that the market does. The calculated alpha

and beta values which comprise the resulting trend line from this regression represent the

normal stock return for that company. Any observations that do not lie on this trend line are

said to be abnormal stock returns. Table 4 shows an example of a regression of the historical

stock returns of the first company in Portfolio NL, Aegon, regressed against the historical

returns of its respective market index, the AEX.

Table 4: Regression of historical returns of Aegon vis--vis historical returns of AEX

From the above regression we can determine that there is a resulting alpha-value of 0.0016269

and a resulting beta-value of 1.614483. The relation between the dependent and independent

values is statistically significant on a 5% level of confidence. The alpha-value is insignificant

on a 5% level of confidence because it would indicate that if the market has a return of zero,


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then the given company would have a 95% chance of a return of alpha. Equation 4 displays the

resulting trend line from these calculations with a positive y-intercept and a positive slope.

0.0016269 + 1.614483 (4)

To further illustrate the relation between a companys stock returns and its indexs returns as

well the concept of abnormal returns, a diagram is a useful resource. Figure 1 shows a graphical

representation of a regression of the returns of Aegon on the returns of AEX, the resulting trend

line which represents normal returns, and all observations of the regression.

Figure 1: Graphical representation of regression of historical returns of Aegon vis--vis

historical returns of AEX

The above diagram shows that a majority of returns of Aegon are expected returns and thus lie

on their trend line, but this is not the case for all 3650 observations in this example. The

variation around the trend line indicates a different value of returns, hence not all returns ofAegon are expected (i.e. normal). This finding leads us to believe that the AEX is not perfectly

efficient in the weak-form, but we are less concerned on whether the AEX is efficient or not,

and more concerned with how relatively efficient it actually is.

Moving forward, we calculate the functions of all expected returns for all companies in

Portfolio NL and Portfolio IT and find the resulting trend lines which represent their normal

returns, as shown above. Abnormal returns, AR, can be expressed by Equation 5:

AR = Y (5)

-1

-0,5

0

0,5

1

1,5

2

2,5

3

3,5

4

-0,15 -0,1 -0,05 0 0,05 0,1 0,15PercentageofReturnsonAegon

Percentage of Returns on AEX


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To rewrite this into our equation for the trend line, as given in Equation 4, we utilize the

following general equation:

AR = Y( + irm) (6)

In Equation 6, Y is the actual return. The variable, , is the constant. The variable i is the

coefficient of the market index and rmis the market return.

We calculate the abnormal returns for all companies and all dates that are available according

to the above illustrated equation. Adding up all observed abnormal returns, we are able to

calculate the cumulative abnormal return for each company. We gather the value of the

cumulative abnormal returns for each of the 65 companies in both portfolios and organize them

for each from smallest to largest. Table 5 and 6 give the values of the cumulative abnormal

returns for Portfolio NL and Portfolio IT.

Table 5: Cumulative abnormal returns for Portfolio NL ranked from smallest to largest

Number Company Ticker Symbol Cumulative Abnormal Returns

22 Unibail-Rodamco UL.AS -0.619626459

1 Aegon AGN.AS -0.326610086

13 ING Group INGA.AS -0.233641469

16 Philips PHIA.AS -0.191947186

3 Akzo Nobel AKZ.AS -0.15524682514 KPN KPN.AS -0.119942357

9 DSM DSM.AS -0.101748321

7 Corio CORA.AS -0.086414801

5 ASML ASML.AS -0.083317924

6 Boskalis BOKA.AS -0.074858901

17 Randstad Holding RAND.AS -0.066573181

20 SBM Offshore SBMO.AS -0.058928503

18 Reed Elsevier REN.AS -0.057899584

23 Unilever UNA.AS -0.053681427

12 Heineken HEIA.AS -0.04590407825 Ziggo ZIGGO.AS -0.025720303

11 Gemalto GTO.AS -0.014018884

21 TNT Express TNTE.AS -0.013410443

2 Ahold AH.AS -0.009533463

10 Fugro FUO.AS -0.004885449

8 Delta Lloyd DL.AS -0.004522176

15 OCI OCI.AS -0.000838758

19 Royal Dutch Shell RDSA.AS 0.004201578

24 Wolters Kluwer WKL.AS 0.01808204

4 ArcelorMittal MT.AS 0.060306549


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Table 6:Cumulative abnormal returns for Portfolio IT ranked from smallest to largest

Number Company Ticket Symbol Cumulative Abnormal Returns

32 Telecom Italia TIT.MI -3.288621675

10 Campari CPR.MI -0.109351153

33 Tenaris TEN.MI -0.059108408

6 Banca Pop Emilia Romagna BPE.MI -0.057358903

15 Exor EXO.MI -0.051727993

8 Banco Popolare BP.MI -0.050712416

40 Yoox YOOX.MI -0.039413352

27 Prysmian PRY.MI -0.037165888

31 Stmicroelectronics STM.MI -0.035683072

34 Terna - Rete Elettrica Nazionale TRN.MI -0.03224069

12 Enel ENEL.MI -0.030502114

35 Tod'S TOD.MI -0.029100857

5 Banca Monte Paschi Siena BMPS.MI -0.028085922

19 Gtech GTK.MI -0.027381132

29 Salvatore Ferragamo SFER.MI -0.02722681

21 Luxottica LUX.MI -0.027009608

24 Mediolanum MED.MI -0.025756345

28 Saipem SPM.MI -0.025628062

36 Ubi Banca UBI.MI -0.017389757

30 Snam SRG.MI -0.015311527

14 Eni ENI.MI -0.01489661722 Mediaset MS.MI -0.007761084

7 Banca Pop Milano PMI.MI -0.007697444

3 Autogrill AGL.MI -0.004849807

13 Enel Green Power EGPW.MI 0.006429264

1 A2a A2A.MI 0.014079185

11 Cnh Industrial CNHI.MI 0.014219847

17 Finmeccanica FNC.MI 0.016331898

25 Moncler MONC.MI 0.017835498

9 Buzzi Unicem BZU.MI 0.018630308

39 World Duty Free WDF.MI 0.02609141

16 Fiat F.MI 0.0527779

26 Pirelli & C PC.MI 0.057762385

38 Unipolasi US.MI 0.099808406

4 Azimut Holding AZM.MI 0.19558042

2 Atlantia ATL.MI 0.565031305

20 Intesa Sanpaolo ISP.MI 0.984056857

23 Mediobanca MB.MI 1.08364014

18 Generali G.MI 1.433350766

37 Unicredit UCG.MI 3.982539888


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Next we find the standard deviation of these cumulative abnormal returns for each portfolio.

We use the standard deviation as a determinant for outliers. If a companys cumulative

abnormal return lies within the standard deviation of the portfolio it is in line with our model,

whereas a company that lies outside the portfolio standard deviation is relatively inefficient

with respect to its portfolio. Table 7 shows the mean and standard deviation of the cumulative

abnormal returns of Portfolio NL and Portfolio IT.

Table 7: Mean and standard deviation of Portfolio NL and Portfolio IT

Portfolio NL Portfolio IT

Mean -0.090667216 0.112954621

Standard Deviation 0.139460922 0.888304929

We now compare each cumulative abnormal return with its respective portfolio standard

deviation and determine which companies lie outside the range and are considered as an outlier.

4.3 Results

Following the empirical testing as explained in section 4.2, we can now determine which

companies show noticeable abnormal returns and which portfolio shows inefficiency to a larger

extent.

4.3.1 Results Of Portfolio NL

Comparing the cumulative abnormal return of the 25 Dutch companies with its respective index

standard deviation shows us that 5 companies lie outside the range of the standard deviation

and are thus considered as inefficient. These companies are: Unibail Rodamco, Aegon, ING-

Group, Philips and Akzo Nobel. Expressing this in percentage terms and referring to our null

hypothesis, we find:

100%20.00%

Hence, 20.00% of the portfolio shows noticeable abnormal return. With a standard deviation of

0.139, there are four negative outliers and one positive outlier. Figure 2 represents the normal

distribution of Portfolio NL and shows where on the normal distribution all companies lie,

based on their cumulative abnormal return. The x-axis represents the value of the cumulative

abnormal return and the index standard deviation.


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Figure 2: Normal Distribution of Portfolio NL

4.3.2 Results Of Portfolio IT

Comparing the cumulative abnormal return of the 40 Italian companies with its respective

portfolio standard deviation shows us that 5 companies lie outside the range of the standard

deviation and are thus considered as inefficient. However, expressing this in percentage terms,

gives us a lower number since Portfolio IT is larger.

540 100%12.50%

Hence, Portfolio IT shows noticeable abnormal return of only 12.50%. The following

companies are considered noticeably inefficient: Telecom Italia, Intesa SanPaolo, MedioBanca,

Generali and UniCredit. Figure 3 represents the normal distribution of Portfolio IT and shows

where on the normal distribution all companies lie, based on their cumulative abnormal return.

Again, the x-axis represents the value of the cumulative abnormal return and the index standard

deviation.


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Figure 3: Normal Distribution of Portfolio IT

4.3.3 Comparative Results Of Portfolio NL and Portfolio IT

From our empirical testing, we are able to determine that Portfolio IT is relatively more efficient

than Portfolio NL, and thus we can make the conclusion that the Italian stock market is

relatively more efficient than the Dutch market. This conclusion leads us reject our null

hypothesis and to believe that one stock market is able to be relatively more efficient thananother. As mentioned in sub-section 4.1, we do not have the capability to comment on the

objective efficiency of the Dutch and Italian stock markets. The experiment that we conduct is

a measure of perceived market efficiency from the perspective of investors. Also our proxies

of the of the Dutch and Italian stock markets, the AEX and the FTSE MIB respectively, are

incomplete samples of the actual Dutch and Italian stock markets themselves. Keeping these

limitations in mind, we still find evidence of a discrepancy of comparative market efficiency.


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5.0 Interpretation Of Results

This chapter interprets our results which are represented in the previous chapter. We find that

the Italian stock market is relatively more efficient than the Dutch stock market and thus one

capital market can be relatively more efficient than another. There are a number of interesting

factors what could be responsible for this difference. Section 5.1 starts with differences and

similarities in the indices of both stock markets. Section 5.2 continues with the composition of

each portfolio based on the industry, where section 5.3 pays attention to information disclosure

and transparency. The final two sections touch upon cross-listings and cultural differences

respectively.

5.1 Mechanism Of AEX and FTSE MIB

Where section 2.4 explains the history of the two stock markets and 2.5 explains the mechanism

of both indices, this section elaborates on these differences and similarities in order to place our

outcome of efficiency. The differences in the histories of the Dutch and the Italian stock markets

can be seen as cause for their differences in relative efficiency. As both markets evolved

simultaneously, they both were subject to different economic events. A stock market does not

exist in a vacuum, and is affected by external occurrences that can have a significant impact on

its development. With regards to the AEX and the FTSE MIB, the indices that measure these

two dynamic markets in 2014, both are capped at 15%. This means that in both our samples, no

single company accounts for more than 15% of the index. When it comes to rules for selection,

when a company can be eligible for entering the index, some differences occur. An overview

is given and an explanation follows.


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Table 8: Rules for selection

Selection Criteri a: FTSE MIB AEX

Free float At least 15% At least 15%

Free float velocity - At least 25%

Ranking based onFree float Marketcapitalization

36 or higher, replacing formerconstituent with the lowestranking

15 or higher, replacing former constituent withthe lowest ranking

If foreign companywants to enter theindex

Comply with BIt requirements interms of dissemination ofinformation

Have Euronext Amsterdam as Market ofReference, or comply with percentages ofactivities/assets/staff that should located in theNetherlands

When can a companyenter?

At quaterly reviews, butexceptions can be made if there's

a vacancy outside quaterlyreview

Inclusion of comapnies takes places at reviewsonly (quaterly)

Source: NYSE Euronext (2014)and London Stock Exchange Group (2014)

The most interesting difference is when it comes to a foreign company entering the index. In

the Netherlands a foreign company should have the Euronext Amsterdam as a Market of

Reference or, if this is not the case, comply with rules about having a certain percentage of its

assets or activities in the Netherlands, or having a certain percentage of Dutch staff. Italy finds

it more important that a foreign company complies with the Borsa Italiana requirements in terms

of dissemination of information (Borsa Italiana, 2014). Here it comes in place that Italy has

visibly stricter rules applying to disclosing information. This finding is in support of our results,

since stricter rules about information disclosures implies a greater efficiency.

Other features of the index mechanisms are quite the same, concerning exclusion criteria and

general composition rules. Also when it comes to announcement policy both indices take thesame amount of time into account and the announcement is by both done by a technical note.

5.2 Composition Of Portfolio By Industry

The components of the FTSE MIB and the AEX can be divided into 19 separate industries. The

majority of the components of the FTSE MIB are in the banking industry, while the majority

of the components of the AEX are in the financial services industry. Table 9 shows the

industries in which the components of Portfolio NL and Portfolio IT are divided, indicated by

the number of companies present in the industry.


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Table 9: Industry division of Portfolio NL and Portfolio IT

Industry Portfolio NL Portfolio IT

Apparel - 5

Automotive - 2

Banking 1 9

Beverages 1 1

Broadcasting - 1

Catering - 1

Chemicals 3 -

Construction - 2

Consumer Goods 2 1

Energy 3 3

Engineering 1 -

Financial Services 4 4

Human Resources 1 -

Postal Services 1 -

Publishing 2 -

Steel & Iron 1 1

Technology 3 5

Telecommunications 2 1

Utilities - 4

Total 25 40

Unlike the financial services industry, banking is required to maintain a certain standard of

transparency. The Basel Committee on Banking Supervision states, in accordance with Basel

II, Under Pillar 3, capital adequacy must be reported through public disclosures that are

designed to provide transparent information on capital structure, risk exposure, and risk

management and internal control processes (JPMorgan Chase & Co., 2014). This transparency

requirement implies greater efficiency on the Italian stock market. The reason why banks are


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mainly absent in Portfolio NL goes back to the creation of the Amsterdam Stock Exchange and

the years there after. According to the comments of Peter Hgfeldt given in the paper of de Jong

& Rell (2005, p. 508), banks played a limited role in the Dutch financial industry and were

therefore not heavily included in the Amsterdam Stock Exchange. For an outside observer

perhaps the most surprising feature of the Dutch financial system is the limited role played by

the banks in the financing of industrial growththroughout the industrialization as well as later.

Banks were specialized in traditional short-term financing and not strongly focused on

operating as a universal bank. Bank noninterventionism became a long tradition from the

beginning on.

Another remarkable observation is what type of industry the outliers represent. Where in

Portfolio NL the outliers are in more diversed industries, Portfolio IT has outliers mainly in thebanking industry. The outliers of Portfolio NL are present in the financial services, banking,

technology and chemicals, whereas the outliers of Portfolio IT are present in

telecommunication, banking and financial services. The fact that the outliers of Portfolio IT are

mainly banks, and hence in Portfolio NL the only bank is an outlier as well, can perhaps be

explained by the global financial crisis in 2008. This lies in the time span of our data and may

have an effect on our outliers, but we cannot make any conclusions about its significance at this

time.

5.3 Information Disclosure and Transparency

Transparency of information and disclosing all available information as a company are concepts

that differ among companies and countries. Since the nature of the Efficient Market Hypothesis

is concerned with stock prices reflecting all available information, it is worthy to investigate to

what extent the Netherlands and Italy disclose information. Figure 4 shows information

disclosure in both countries ranging from 2005 until 2013. The extent of disclosing information,

given by the y-axis, ranges from 1 to 10, with 1 being a low degree of disclosing informationand 10 a very high degree. As can be seen from the figure, Italy has a value of 7, where the

Netherlands has a value of only 3, increasing to 4 in the last two years.


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Figure 4: Degree of information disclosure in the Netherlands and Italy

Source: World Data Bank (2014)

This means that Italy discloses more business information than the Netherlands. According to

our assumptions, an efficient market should disclose more information than a less efficient

market. The reason why information disclosure is quite low in the Netherlands is because of a

higher degree of business secrecy. As Peter Hgfeldt in the paper of de Jong & Rell (2005)

states, the Dutch companies have a tradition of secrecy to protect Dutch firms. This goes backto the beginning of the creation of the Amsterdam Stock Exchange.

5.4 Cross-Listings and Time Zones

The components of Portfolio IT are present on more stock exchanges and in more time zones

than the components of Portfolio NL. The companies that comprise the Italian stock market are

listed on a greater number of different stock markets throughout the world. Considering this

greater variety of stock markets, the FTSE MIB should be more transparent because there is a

greater exposure to investors than the AEX. The components of the FTSE MIB are listed on 60separate exchanges while the components of the AEX are listed on only 46 separate exchanges.

The companies that comprise the Italian stock market are listed on stock exchanges that reside

in a great number of separate time zones around the world. The fact the FTSE MIB is present

in more time zones than the AEX implies that the prices of its components have to adjust at a

faster and more consistent rate. Table 10 shows the time zones in which the components of

Portfolio NL and Portfolio IT are listed.

0

1

2

3

4

5

6

7

8

2005 2006 2007 2008 2009 2010 2011 2012 2013

BusinessExtentofDisclosure

Index

Year

Italy

Netherlands


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Table 10: Time zones in which Portfolio NL and Portfolio IT are present in, indicated with the

number of companies

Time Zone Portfolio NL Portfolio IT

GMT -07:00

GMT -06:00 1

GMT -05:00 7 5

GMT -04:00

GMT -03:00 1

GMT -02:00

GMT -01:00

GMT 00:00 3

GMT +01:00 36 53

GMT +02:00

Total Listings 46 60

5.5 Cultural Differences

According to Gerard Hendrick Hofstede (1980), the cultural values and traits of the Netherlands

and Italy can be quantified and thus compared with one another. These cultural traits haveimplications on the manners in which both nations conduct themselves economically. One

relevant measure of economic culture that is calculated by Hofstede is the Uncertainty

Avoidance Index. Hofstede explains how uncertainty avoidance is related to traditionalism and

a lack of desire to be progressive by stating, Coping with the inevitable uncertainties in life is

partly a non-rational process (Hofstede, 1980, p. 161).

Using this index to compare the Netherlands and Italy, with a score of 100 implying maximum

uncertainty avoidance and a score of 0 implying minimum uncertainty avoidance, the

Netherlands is at 53 while Italy is at 75. We can thus determine that the Italian culture facilitates

a greater degree of uncertainty avoidance in its economics. Uncertainty avoidance in Italy can

be seen its lack of innovation in the banking industry, and its stronger hold on traditional

banking services. Figure 5 shows the percentage of individuals in the Netherlands, the European

Union, and Italy who use internet banking.


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Figure 5: Percentage of individuals in the Netherlands, European Union, and Italy who use

internet banking from 2005 through 2013

Source: Eurostat (2014)

As is shown in the figure above, the Netherlands is consistently more invested in innovative

banking services than is Italy. This is partially the result of uncertainty avoidance present in

Italian culture, which also implies a relatively stronger focus on transparency in its banking

industry. The transparency in the banking industry, which is a result of Italian cultural values,

may contribute to the greater relative efficiency of the Italian stock market.

0%

20%

40%

60%

80%

100%

2005 2006 2007 2008 2009 2010 2011 2012 2013

PercentageofInternet

BankingUsers

Year

European Union (27 countries)

Netherlands

Italy


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6.0 Conclusion and Recommendation

The Efficient Market Hypothesis has been developed for decades and has been well established

as a valid school of thought within finance. There is a vast amount of empirical research on the

hypothesis, but there are still noticeable contradictions and argument among economists. We

take into account the strengths of both sides of the debate, but based on our results we come to

the conclusion that one stock market can be relatively more efficient than another.

When measuring cumulative abnormal returns, it becomes clear that our Portfolio NL shows a

greater percentage of companies that lay outside of its standard deviation than does our Portfolio

IT. This empirical result leads us to believe that the FTSE MIB is relatively more efficient than

the AEX, and thus the Italian stock market is relatively more efficient than the Dutch stock

market in the weak form. We interpret these findings to be the product of several factors. The

Italian stock exchange is composed of more companies in the banking industry which are

subject to stricter regulations on information disclosure. The components of the Italian stock

exchange are cross listed on more stock exchanges and in more time zones. Finally, the Italian

stock market and the FTSE MIB require a greater degree of information disclosure for the entry

of foreign companies.

There are factors that exist outside the scope of our research, but that we still believe may have

an effect on comparative market efficiency and are thus recommended for further research. In

this thesis we are not able to conduct a comparison of the semi-strong and strong forms of

market efficiency. If the same assessment of comparative market efficiency can be applied to

these two forms, there would be the possibility to obtain more telling results in addition to our

own findings.We also believe that the role of behavioral psychology of investors in capital

markets plays a role in the explanation of comparative market efficiency. The Efficient Market

Hypothesis makes the assumption that all investors are rational, but behavioral finance

contrarily indicates that humans are indeed systematically irrational. If the humans that

comprise stock markets across nations are proven to be irrational, this could lead to a

discrepancy in the observed efficiency of different stock markets. A comparative cultural study

could potentially compliment this finding as well if there is a way to indicate that one nation

has tendencies to be more or less rational than another.


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Comparative Market Efficiency: A comparison of the efficiency of the Dutch and Italian stock markets

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