Market Efficiency and Limits to Arbitrage in Advanced Emerging Markets An independent thesis submitted in fulfilment of the requirements for the degree of Doctor of Philosophy in Accounting and Finance (University of Newcastle)
Mostafa Seif BSc in Civil Engineering (University of Tehran) Master of Project Management (University of New South Wales) Master of Applied Finance (University of Newcastle)
Newcastle Business School Faculty of Business and Law UNIVERSITY OF NEWCASTLE
August 2016
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Statement of Originality
The thesis contains no material which has been accepted for the award of any other degree or diploma in any university or other tertiary institution and, to the best of my knowledge and belief, contains no material previously published or written by another person, except where due reference has been made in the text. I give consent to the final version of my thesis being made available worldwide when deposited in the University’s Digital Repository**, subject to the provisions of the Copyright Act 1968.
**Unless an Embargo has been approved for a determined period.
Mostafa Seif August 2016
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Statement of Authorship
I hereby certify that the work embodied in this thesis contains a published paper/s/scholarly work of which I am a joint author. I have included, as part of the thesis, a written statement endorsed by my supervisor attesting to my contribution to the joint publication/s/scholarly work.
Signed: ……………………………… Date: ………………………………..
The following conference papers are the outcomes of the following thesis:
• Seif, M., Docherty, P., Shamsuddin, A., (2016) “Seasonal anomalies in
advanced emerging stock markets.” Conference on Applied Financial
Modelling, 4 -5 February, Melbourne, Australia
• Seif, M., Docherty, P., Shamsuddin, A., (2016) “Limits to arbitrage and
the MAX effect in emerging markets.” Portsmouth – Fordham Conference
on Banking & Finance, 24 – 25 September, Portsmouth, UK
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Acknowledgement
First and foremost, I would like to express my deepest gratitude to my research supervisors, Professor Abul Shamsuddin and Dr. Paul Docherty for supporting me through all the ups and downs of my PhD candidacy. I thank my supervisors for all their help and support and their patience and guidance through all these years.
Abul has always been a professional mentor. His work ethics and his kindness are among his various qualities that I look up to. He has greatly improved my dissertation with his insightful comments and his statistical expertise. Paul has been more than a supervisor to me and helped me as a friend even before I enrolled as a PhD candidate. I am extremely fortunate to be able to work with two supervisors who have endlessly supported both my career and personal development. Without them, I would never have been able to go this far.
I would like to express my deepest appreciation to the love of my life,
Maedeh, who spent sleepless nights with me and was always my support in the moments when there was no one to answer my queries. She literally put my career before hers and supported me through all these years. I thank her for supporting me spiritually throughout writing this thesis. Her understanding and love during the past few years was in the end what made this dissertation possible.
Last but not least, I would like to thank my parents, Valiollah Seif and
Shayesteh Moghaddam, whom I could not be where I am today without them. I thank them for all the moral support and the amazing chances they have given me over the years. They have been so supreme, nurtured my learning and supported my dreams. Writing this thesis was impossible without their encouragement.
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Abstract
This thesis has two key motivations. The first is to undertake a comprehensive examination of the market characteristics and institutional features across nine
‘advanced emerging’ stock markets: Brazil, Czech Republic, Hungary, Malaysia,
Mexico, Poland, South Africa, Taiwan and Turkey. After identifying that these markets comprise characteristics that may restrict rational investors from arbitraging away identifiable mispricing, the second purpose of this thesis is to examine the level of market efficiency and the relation between mispricing and limits to arbitrage. Given limitations with the depth and quality of accounting information across emerging markets, the issue of market efficiency is examined by testing whether returns and volatility-based anomalies that have been identified in developed markets are also evidenced across this sample. More specifically, this thesis examines the prevalence of seasonality in both returns and volatility of returns with respect to five calendar anomalies that have been identified in the context of developed markets. In addition, this thesis examines whether there is evidence of a negative relationship between maximum one-day returns and subsequent monthly returns (referred to as the MAX effect) in advanced emerging markets. Despite an extensive number of studies documenting evidence of seasonal anomalies and the MAX effect in developed markets, which indicates that these markets are less than perfectly efficient, this thesis provides the first comprehensive examination of this issue across advanced emerging markets. This thesis also expands the existing literature by examining the potential sources of such anomalous returns and the existence of mispricing in these markets by
V | Page testing whether the returns can be explained by risk-based pricing models or time series variation in limits to arbitrage.
The results of this thesis provide evidence of the existence of strong anomalous returns, which indicates a high level of mispricing across advanced emerging stock markets. Specifically, this thesis finds that, on average, returns are higher during the month of December, the 44th week of the year, Fridays and pre- and post-holidays; and these anomalous returns are not explained by seasonal variation in volatility. Moreover, this thesis reports evidence of a strong MAX effect that is persistent after controlling for size, book to market ratio, market beta, momentum, short-term return reversals and liquidity. The magnitude of the mispricing associated with the MAX effect appears to be higher in advanced emerging markets compared with developed markets. The zero-investment returns generated by the MAX effect are shown to co-vary with time series variation in limits to arbitrage. Taken as a whole, these results suggest a lower level of efficiency across advanced emerging markets that can be explained by the market characteristics and institutional features that restrict the ability of rational investors to arbitrage away mispricing. The results reported in this thesis can therefore allow investors to better understand the characteristics of risk and returns in advanced emerging markets in order to develop improved asset pricing models in the context of these markets. In addition, the demonstrable link between asset pricing anomalies and limits to arbitrage may provide policy makers with a framework for improving the efficiency of their stock markets by mitigating market frictions through investor protection measures, and relaxation of capital controls and short-selling restrictions, among others.
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Table of Contents
Chapter 1 : Introduction ...... 1
1.1. Thesis background ...... 2
1.2. Research questions ...... 7
1.2.1. Do advanced emerging markets exhibit seasonal patterns in stock
returns? ...... 8
1.2.2. Do advanced emerging markets exhibit the MAX effect? ...... 19
1.3. Thesis outline ...... 21
Chapter 2 : Overview of advanced emerging markets ...... 25
2.1. Introduction ...... 26
2.2. Regulatory and institutional framework in emerging markets ...... 29
2.2.1. Equity market liberalization ...... 29
2.2.2. Exchange rate regime ...... 40
2.2.3. Social institutions and culture ...... 45
2.2.4. Corporate governance ...... 50
2.2.5. Fiscal year ...... 60
2.3. Market characteristics ...... 61
2.4. Stock return and volatility patterns ...... 67
2.5. Geographical location ...... 76
2.6. Conclusion ...... 80
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Chapter 3 : Seasonal anomalies in advanced emerging stock markets ...... 84
3.1. Introduction ...... 85
3.2. Literature review ...... 87
3.2.1. The month of the year effect ...... 87
3.2.2. The other January effect (January barometer) ...... 89
3.2.3. The day of the week effect ...... 90
3.2.4. Holiday effect ...... 92
3.2.5. The week of the year effect ...... 93
3.3. Data ...... 95
3.4. Methodology and results ...... 103
3.4.1. The month of the year effect ...... 103
3.4.2. The other January effect ...... 110
3.4.3. The day of the week effect ...... 112
3.4.4. Holiday effect ...... 117
3.4.5. The week of the year effect ...... 123
3.5. Robustness check ...... 128
3.6. Summary ...... 129
Chapter 4 : Limits to arbitrage and the MAX effect in emerging markets ...... 133
4.1. Introduction ...... 134
4.2. Data ...... 140
4.3. Methodology and results ...... 146
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4.3.1. Univariate portfolio sorts ...... 146
4.3.2. Bivariate portfolio sorts...... 155
4.3.3. Time series regression analysis ...... 162
4.3.4. Panel regression analysis...... 176
4.4. The MAX effect and the limits to arbitrage hypothesis ...... 182
4.5. Conclusion ...... 190
Chapter 5 : Conclusion ...... 194
5.1. Introduction ...... 195
5.2. Key findings ...... 196
5.3. Implications of key findings ...... 202
5.4. Limitations and directions for future research...... 204
References ...... 207
Appendices ...... 226
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List of Figures
Figure 2.1: The value of one US dollar invested in the South African Rand ...... 41
Figure 2.2: The value of the Brazilian Real currency per US dollar...... 42
Figure 2.3: The value of the Malaysian Ringgit currency per US dollar ...... 43
Figure 2.4: The value of the Turkish Lira per US dollar ...... 44
Figure 2.5: Average Monthly Returns ...... 68
Figure 2.6: Average Daily Stock Returns ...... 69
Figure 2.7: Correlation of daily market returns with the US market ...... 74
Figure 2.8: Two-year correlation of daily market returns with the US market..... 75
Figure 2.9: Conditional correlation with the US stock market ...... 76
Figure 3.1: The average weekly returns in advanced emerging markets (%) ..... 126
Figure 3.2: The conditional volatility and mean returns for individual weeks
during the year in South Africa...... 127
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List of Tables
Table 2.1 Advanced emerging equity market liberalization dates ...... 31
Table 2.2: Characteristics of equity markets based on the categorization of the
Financial Times Stock Exchange (FTSE, 2014) ...... 34
Table 2.3: Country level regulatory environment and market characteristics ...... 38
Table 2.4: Stock exchange holidays in advanced emerging markets ...... 47
Table 2.5: Disclosure items of International Standard of Accounting and
Reporting (ISAR) benchmark from the United Nation Conference on Trade
and Development (UNCTAD)...... 54
Table 2.6: The corporate governance environment in advanced emerging markets
versus the US ...... 59
Table 2.7: Country level information and market characteristics of advanced
emerging stock market ...... 64
Table 2.8: Descriptive statistics of the daily returns and volatility characteristics of
emerging stock markets versus US...... 72
Table 2.9: The geographical location of advanced emerging markets...... 80
Table 3.1: General market information on advanced emerging markets ...... 98
Table 3.2: Descriptive statistics of market index returns in the local currency .. 102
Table 3.3: Results for the month of the year effect ...... 106
Table 3.4: Results for the other January effect (denominated in local currency of
each country)...... 111
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Table 3.5: Results for the day of the week effect (denominated in local currency of
each country)...... 115
Table 3.6: Descriptive statistics of pre-holiday and post-holiday returns ...... 119
Table 3.7: Regression results for pre-holiday and post-holiday effect for advanced
emerging markets (denominated in the local currency of each country) ..... 121
Table 3.8: Results for week 44 effect for advanced emerging markets
(denominated in local currency of each country) ...... 125
Table 4.1: Total number of listed stocks (Number Passing 30% Price Change
Filter) ...... 142
Table 4.2: Descriptive statistics of monthly returns...... 145
Table 4.3: Average monthly returns of the portfolios sorted based on MAX..... 148
Table 4.4: Average monthly returns of the portfolios sorted by Size ...... 151
Table 4.5: Average monthly returns of the portfolios sorted by book to market
ratio ...... 152
Table 4.6: Average monthly returns of the portfolios sorted by momentum ..... 153
Table 4.7: Average monthly returns of the portfolios sorted by liquidity ...... 154
Table 4.8: Bivariate sorting results on MAX and size ...... 157
Table 4.9: Bivariate sorting results on MAX and book to market ratio ...... 160
Table 4.10: Alpha results from time series regressions on portfolios sorted on
MAX ...... 164
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Table 4.11: Alpha results from time series regression on double sorted Size and
MAX portfolios (3 Factor Model) ...... 167
Table 4.12: Alpha results from time series regression on double sorted Size and
MAX portfolios (5 Factor Model) ...... 169
Table 4.13: Alpha results from time series regression on double sorted BV/MV
and MAX portfolios (3 Factor Model) ...... 172
Table 4.14: Alpha results from time series regression on double sorted BV/MV
and MAX portfolios (5 Factor Model) ...... 174
Table 4.15: Panel regression results ...... 178
Table 4.16: Time series regression results for the relation between the MAX effect
and the proxies for limits to arbitrage ...... 186
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List of Appendices
Appendix I: Descriptive statistics of market index returns in the US Dollar...... 227
Appendix II: Results for the month of the year effect ...... 229
Appendix III: Results for the day of the week effect (denominated in the US
Dollar) ...... 230
Appendix IV: Regression results for pre-holiday and post-holiday effect for
advanced emerging markets (denominated in the US Dollar) ...... 231
Appendix V: Results for week 44 effect for advanced emerging markets
(denominated in the US Dollar) ...... 232
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Chapter 1 : Introduction
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1.1.Thesis background
The efficient market hypothesis of Fama (1970) states that the equilibrium prices of stocks should be equal to their intrinsic value as arbitragers correct any mispricing in a short period of time. A large body of literature has emerged which empirically examines the efficient market hypothesis across a range of different markets. While developed markets are over represented in this literature, there is paucity of academic evidence in the context of emerging markets. Further analysis of the level of emerging market efficiency is important, given the ongoing debate in the literature regarding the efficiency of emerging markets relative to developed markets. The common notion is that there is a higher level of limits to arbitrage reflecting the extra risk that investors have to face in emerging markets compared to developed markets. More specifically, in addition to model risk and arbitrage implementation costs, arbitragers bear extra fundamental risk arising from political instability and economic policy uncertainty in emerging markets. As a result of these limits to arbitrage transactions, emerging markets may be expected to have a greater level of mispricing and hence be less efficient compared to developed markets (Bekaert & Harvey, 2014). In contrast with this conventional wisdom, Griffin, Kelly, and Nardari (2010) claim that emerging markets are as efficient as developed markets. They argue that mispricing cannot be arbitraged away in emerging markets because of higher transaction costs and hence, no abnormal return can be achieved in these markets (Griffin et al., 2010).
The common way to test the market efficiency is to study the adequacy of an asset pricing model such as the Capital Asset Pricing Model (CAPM) in
2 | Page explaining stock returns. The CAPM builds upon portfolio theory to propose that the expected returns of an asset can be entirely explained by market risk (Sharpe,
1964). While the CAPM is theoretically appealing, a number of studies since the
1970s have identified empirical limitations of this model (Banz, 1981; Basu,
1977). Studies have shown that many other factors, known as ‘anomalies’, explain returns in addition to the market risk factor proposed by the CAPM (Fama &
French, 1992). For instance, Basu (1977) finds that the price earnings ratio of firms can predict future returns. Banz (1981) shows that stocks with lower market capitalization have higher returns. Jegadeesh and Titman (1993) provide evidence that past winners outperform past losers. Rozeff and Kinney (1976) find evidence of seasonal variation in stock returns.
The level of mispricing attributed to anomalous returns often persists in the market for decades and arbitragers do not take advantage of such mispricing; an observation that is at odds with the efficient market hypothesis. Shleifer and
Vishny (1997) claim that the level of mispricing is directly related to the level of the limits of arbitrage in the market. There are many studies in the academic literature regarding limits to arbitrage and the variables affecting arbitragers in the market. These variables have been shown to prevent arbitragers from exploiting mispricing, and cause anomalous returns to persist. For instance, higher transaction costs in the market reduce the profits of arbitragers (Barberis &
Thaler, 2003). On the other hand, lower liquidity and higher volatility in the market introduce additional risk for arbitragers (Shleifer & Vishny, 1997).
Most of the existing studies of market efficiency have focused on developed markets, particularly in the United States. Despite the dramatic growth
3 | Page of emerging markets in the last few decades, very few empirical studies have been carried out to test anomalies in the context of emerging markets. Emerging markets have unique characteristics that can affect stock returns and volatility. For instance, the regulatory and institutional framework, the exchange rate regime, the regulations regarding tax, the dominant culture and religion in the country, the geographical location and the corporate governance environment are all different in emerging markets compared to developed markets. These unique characteristics can affect limits to arbitrage and therefore affect the return and volatility characteristics of stocks in these markets. The magnitude and persistence of stock return anomalies may differ in emerging markets compared to more developed markets given these different market and institutional features. For this reason, developing a better understanding of the characteristics of individual markets can better inform research into the efficiency of those markets.
Despite the paucity of academic evidence regarding emerging markets, they are of great interest to investors, as emerging markets’ returns tend to have a lower correlation with returns in developed markets. Therefore, substantial diversification benefits can be achieved by investing in emerging markets
(Harvey, 1995). Furthermore, the appropriate management of risk requires investors to develop a better understanding about returns and volatility in less developed markets. The aim of this thesis is to conduct a comprehensive study into the efficiency of ‘advanced emerging’ markets by investigating the persistence and drivers of returns and volatility-driven anomalies. The research will contribute to the empirical literature on asset pricing in emerging markets, resulting in a better understanding of stock return patterns and contributing to the
4 | Page ongoing debate regarding the relative efficiency of developed and emerging markets.
The paucity of academic evidence in the context of emerging markets can be partially explained by lower levels of information disclosure and a resultant lack of reliable accounting data for individual firms in these markets
(Rouwenhorst, 1999). In the light of this limitation, this thesis will study the specific market characteristics of advanced emerging markets, examine the sample of anomalies based on market data rather than accounting data, and use the results to draw inferences about the efficiency of emerging markets. More specifically, this thesis will examine the prevalence of seasonality in stock returns
(calendar anomalies) and examine whether extreme past returns can explain subsequent monthly returns (the MAX effect). The drivers of these returns will also be investigated by testing whether the returns on these anomalies can be explained by either risk or time-varying limits to arbitrage. Thus, this thesis contributes to the academic literature by testing for the existence and drivers of asset pricing anomalies in the context of advanced emerging markets, providing a test of the efficacy of the efficient market hypothesis within the context of these markets.
Advanced emerging stock markets provide an ideal environment to perform out-of-sample tests for anomalies that have been found to exist in developed markets (Lo & MacKinlay, 1990). In addition, the different characteristics of emerging stock markets compared to more developed markets provide an opportunity to examine the competing theoretical explanations proposed for these anomalies. A small number of studies have investigated the
5 | Page stock return patterns and the existence of the MAX effect in emerging markets.
Most of these studies only examine a short sample period for only a small number of emerging markets (Ahsan & Sarkar, 2013; Al-Saad & Moosa, 2005; Alagidede,
2013; Alrabadi & AL-Qudah, 2012; Nartea, Wu, & Liu, 2014; Nartea & Wu,
2014).
This thesis will study the unique market characteristic of advanced emerging countries with a main focus on seasonality in stock returns and volatility, and the significance of past extreme positive returns in pricing stocks.
The results are expected to develop a better understanding of anomalous returns in advanced emerging markets. This research is of particular interest for investors who want to take advantage of market mispricing. Subsequently, this thesis will enable the development of asset pricing models that are more adequate for explaining stock returns in advanced emerging markets. Furthermore, this thesis can be useful for policy makers in emerging markets because it identifies the particular market characteristics in advanced emerging countries that may mitigate mispricing and anomalous returns. Such deviation of stock prices from their intrinsic value can increase volatility within these markets, which may have an adverse effect on market stability. Improving these market characteristics may result in mitigation of market frictions and mispricing, and thus reduce market volatility and improve the efficiency of the market. For instance, improving the corporate governance environment in the market may reduce information asymmetry, which may result in lower levels of mispricing. The following section explains the research questions related to the following empirical chapters of this thesis.
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1.2.Research questions
For the first step, this thesis studies the specific characteristics of advanced emerging markets and compares them to the US market as the benchmark for developed markets. This is because these characteristics can affect the degree of mispricing and limits to arbitrage and, therefore, the magnitude of anomalous returns in stock markets. Subsequently, after analysing the key financial and economic characteristics of advanced emerging countries in Chapter 2, this thesis proceeds to provide an empirical examination of anomalous returns in these markets. More specifically, Chapter 3 of this thesis examines the prevalence of five seasonal anomalies while Chapter 4 reports the results for the persistence of the MAX effect in advanced emerging markets. These two empirical studies will test the weak and the semi-strong form efficiency of nine countries classified as advanced emerging. The sample of countries that is examined comprises nine countries that are classified as advanced emerging markets by the Financial Times
Stock Exchange (FTSE). The sample comprises Brazil, Czech Republic, Hungary,
Malaysia, Mexico, Poland, South Africa, Taiwan and Turkey (FTSE, 2014). The following subsections provide information regarding each of the anomalies tested in this thesis. Each of the following subsections are related to one of the research questions in this thesis and describes the motivations of the related empirical work, any specific research issues, the discussion of the findings and the contribution it makes to academic literature.
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1.2.1. Do advanced emerging markets exhibit seasonal patterns in stock
returns?
1.2.1.1.The ‘month of the year’ effect
Voluminous literature examines the existence and the pervasiveness of the month of the year effect in the US and other developed markets. Many studies report evidence of the January effect, as higher than average returns occur in the month of January than in other months of the year (Rozeff & Kinney, 1976). Several studies proposed possible explanations of their results. These propositions attempt to connect the abnormality in the market with a unique characteristic of the stock market under study. For instance, Branch (1977) proposed the tax loss selling hypothesis and Lakonishok and Smidt (1988) introduced the ‘window dressing’ hypothesis, which related the January effect in the US market to the January to
December fiscal year. It should be considered that these characteristics might be specific to the US market and might be different in other markets. The sample of advanced emerging markets provides a unique laboratory to test the month of the year effect because it can also test the proposition about the underlying driver of the anomaly. In this case, other countries exist which have a different fiscal year compared to the US market. For instance, in the sample of advanced emerging markets, South Africa has an April to March fiscal year.
This thesis will first, empirically study the month of the year effect in nine advanced emerging stock markets, and then identify the factors contributing to the month of the year effect in these markets. The related hypothesises are as follows:
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H1A: There are differences in the mean returns across months in
advanced emerging markets.
This hypothesis is examined using time series regressions. Contrary to results reported in the US and other developed markets, average returns are higher in the month of December in six out of nine advanced emerging markets. Note that the higher average stock returns in December are at odds with the tax-loss selling and window dressing propositions in which investors sell their loser stocks at the end of the fiscal year and buy them back at the beginning of the next fiscal year.
Given five out of nine advanced emerging markets have a January to December fiscal year-end; evidence of higher returns in December across these markets is contradictory to the hypotheses put forward in previous studies. Moreover, this study finds no evidence of the April effect in South Africa, despite a March fiscal year-end in that country. These results provide evidence that the tax loss selling and window dressing hypotheses cannot be the main contributor of the January effect in the US and other developed markets.
H1B: There are differences in the volatility of stocks returns across
months in advanced emerging markets.
This study finds no evidence of time varying volatility of returns using the ARCH
LM test (Engle, 1982). This result is important because it rejects the possible risk- based explanation of the month of the year effect in advanced emerging markets and confirms that the amount of total risk investors have to take for holding stocks is statistically constant in different months during the year.
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1.2.1.2.The other January effect
The other January effect (also known as the ‘January barometer’) relates to the predictive power of the stock returns in the month of January for the stock returns during the rest of the year. A number of researchers have conducted empirical work to test the efficacy of this seasonal anomaly in the US (Cooper, McConnell,
& Ovtchinnikov, 2006) and other developed markets (Hensel & Ziemba, 1995), providing evidence to suggest a positive correlation between aggregate market returns in the month of January and the subsequent eleven months. It has been argued that the reason that this pattern in returns exists is the ‘institutional investment committee’ theory, whereby institutions plan their future investments based on their January performance (Little & Albrecht, 2006). However, despite the increase in the proportion of institutional investors in developed markets, recent studies cast doubt on the pervasiveness of ‘the other January effect’
(Marshall & Visaltanachoti, 2010).
Despite the mixed evidence regarding the efficacy of the other January effect in the US and developed markets, there has been no comprehensive study across the broad sample of emerging markets. The purpose of this research is to examine the existence of this anomaly in the sample of advanced emerging markets and to verify if this is context dependent. The sample of advanced emerging markets provides an ideal laboratory to perform out-of-sample tests because of the unique and different market characteristics of advanced emerging markets relative to developed markets. For instance, emerging markets comprise a lower proportion of institutional investors and, therefore, the results of this study
10 | Page can be used to evaluate the institutional investment committee theory proposed by previous studies.
The related hypothesis is as follows:
H2: There is a relationship between market returns in January and the
subsequent eleven months in advanced emerging markets.
This study tests if the stock returns during the eleven months following a positive
January (February to December) are statistically different from the stock returns during the same calendar months following a negative January. The results show that Brazil is the only one out of nine advanced emerging markets that exhibits statistically different returns following positive and negative January returns. It could be argued that the reason for the lack of robust evidence of the other
January effect compared with the results reported by Cooper et al. (2006) in the
US market might be the lower proportion of institutional investors in emerging markets. Given Little and Albrecht (2006) argue that this phenomena is explained by the institutional investment committee theory, it may be expected that there is no evidence of the other January effect across markets that are characterised by a low proportion of institutional investors. On the other hand, these results may also support the argument of Bohl and Salm (2010) that the other January effect is the result of data snooping and it is not an international phenomenon.
1.2.1.3.The ‘day of the week’ effect
There are number of studies that document evidence of statistically significant differences in stock returns across different days of the week. Many studies find
11 | Page lower average returns on Mondays and higher average returns on Fridays compared to other days during the week in the US (Cross, 1973; Lakonishok &
Smidt, 1988) and other developed markets (Dubois & Louvet, 1996; Kiymaz &
Berument, 2003). There are also studies that examine the existence of the day of the week effect in small samples of emerging markets. However, these studies find contrary results compared to developed markets and argue that the day of the week effect has different patterns in different markets. For instance, many studies provide evidence of lower average returns on Tuesday in Thailand and Malaysia
(Brooks & Persand, 2001), Japan (Jaffe & Westerfield, 1985) and Australia, Japan and Singapore (Condoyanni, O'Hanlon, & Ward, 1987).
There are many hypothesises as to why different weekdays have different average returns. For instance, Campbell and Hentschel (1992) argue that the amount of risk in stock markets varies on different days of the week, which may cause investors to have a different required rate of returns on different days of the week. Consistent with this theory, Kiymaz and Berument (2003) and French and
Roll (1986) provide evidence that the volatility of stock returns is statistically different during different days of the week. On the other hand, Aggarwal and
Rivoli (1989) proposed another hypothesis underlying the lower Tuesday returns in many Far East countries. They argue that the lower average Tuesday returns in markets of this region might be due to cross-country return correlations and the lower average Monday returns in the US market. Note that these countries have more than eight hours of time differential with the US market and, therefore, can reflect the lower Monday returns in the US market on the next trading day.
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This thesis contributes to the academic literature by first examining the existence of the day of the week effect in stock returns and volatility across a broad sample of advanced emerging markets. Second, by testing the specific patterns in volatility of returns on different days of the week in advanced emerging markets, this study can examine the efficacy of the proposition of
Campbell and Hentschel (1992) that this anomaly is caused because stock returns have different degrees of volatility during different days of the week. Third, advanced emerging markets are spread around the world from the Far East
(Malaysia and Taiwan) to the Far West (Brazil and Mexico). Therefore, by testing the day of the week effect in the sample of advanced emerging markets, this thesis can test the efficacy of the proposition of Aggarwal and Rivoli (1989) that the time zone differential has an effect on specific patterns of stock markets during the week.
The related hypotheses are as follow:
H3A: There are differences in the average stock returns in advanced
emerging markets across different days of the week effect.
This study performs time series regression equations with ‘dummy’ variables for each day of the week and finds evidence of a day of the week effect in stock returns in seven out of nine advanced emerging markets. More specifically, this study finds higher average returns on Fridays compared to Mondays (similar to developed markets) in six out of nine advanced emerging markets. Moreover, the result shows higher average stock returns on Wednesday and Thursday in South
Africa and no significant pattern in Hungary and the Czech Republic. Therefore,
13 | Page this result can confirm that the day of the week effect is an international phenomenon and is not a characteristic specific to the US and developed markets.
On the other hand, this study finds no evidence of the lower average returns on
Tuesdays in advanced emerging markets, especially Malaysia and Taiwan.
Therefore, the result of this thesis cannot support the argument of Aggarwal and
Rivoli (1989) that time zone differentials of the stock market with the US market can affect the day of the week effect.
H3B: There are differences in the volatility of stock returns across days
in advanced emerging markets.
The ARCH LM test suggests that the volatility of daily stock returns is constant over time in advanced emerging markets. Therefore, the result of this study does not support the argument of Campbell and Hentschel (1992) that variation in daily volatility of stock returns within a week causes variation in expected returns and consequently, the day of the week effect in stock returns.
1.2.1.4.The ‘holiday effect’
Previous studies show that the average stock returns on days preceding holidays and the first trading day after a holiday are higher compared to other trading days in the US and other developed markets (Fields, 1931; Kim & Park, 1994;
Lakonishok & Smidt, 1988). Despite an extensive number of studies documenting this holiday effect in developed markets, only a few studies have examined it within emerging markets. For instance, Chan, Khanthavit, and Thomas (1996) examined Asian stock markets and Bley and Saad (2010) studied the Middle
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Eastern stock markets. This thesis will examine the pervasiveness of the holiday effect in the broader sample of advanced emerging markets.
By conducting this study, this thesis can contribute to the academic literature in different ways. First, the sample used in this study provides an ideal laboratory to conduct out-of-sample tests for the holiday effect that has been documented in developed markets. Given that the nature of national holidays are different in many emerging countries compared to those which have developed markets, this sample can be used to determine whether the holiday effect in stock returns is dependent on the type of public holiday. There is a range of cultures, religion and ethnicity in the sample of advanced emerging markets that makes the specific public holidays examined in this study unique. For instance, Islamic holidays in Malaysia and Chinese New Year holidays in Taiwan and Malaysia are unique relative to developed markets. Second, this thesis can examine the efficacy of the hypothesis that has been proposed for the holiday effect. More specifically, as short selling is prohibited and not accessible in advanced emerging markets, this study can test the ‘inventory adjustment’ hypothesis proposed by Ariel
(1990), in which investors’ tendency to buy back their short sold stocks before holidays causes higher average returns on days preceding holidays. The related hypothesis is as follows:
H4: There are higher returns in days preceding public holidays in
advanced emerging markets.
This study performs time series regressions of daily returns on the relevant dummy variables and finds strong evidence of a holiday effect, especially the
15 | Page post-holiday effect in advanced emerging markets. The post-holiday effect is significant in seven out of nine advanced emerging markets with the exception of
Taiwan and Turkey. In fact, the average stock returns on post-holiday trading days are 20 times higher compared to other trading days in advanced emerging markets. The result of this study provides evidence that the holiday effect is independent from the nature of the national holiday, or the dominant culture and religion of the country. Moreover, the result of this analysis shows that the pre- holiday effect is weaker in advanced emerging markets compared to developed markets. As short selling is limited and not easily accessible in emerging markets, this finding may support the argument of Ariel (1990) that investors’ tendency toward closing short positions before holidays is the main contributor of the pre- holiday effect.
1.2.1.5.The ‘week of the year effect’
The week of the year effect implies that stock returns have different average weekly returns during the year. The study by Levy and Yagil (2012) show that the 44th week of the year, which starts 29th October, has higher average returns compared to other weeks during the year in 20 developed markets including the
US. Their result is statistically robust in 19 out of 20 markets. There are many hypothesises behind this puzzling seasonal pattern in returns. For instance, it has been argued that this effect can be explained by the seasonal affective disorder
(SAD) hypothesis raised by Kamstra, Kramer, and Levi (2003) in which, the number of hours of daylight can significantly affect the behaviour of investors.
Note that the 44th week of the year is the week in which daylight savings
16 | Page concludes in most of the countries, resulting in an earlier sunset that may have a negative effect on mood.
Despite the pervasiveness of the week of the year effect in developed markets, no studies to date have examined this seasonal anomaly in the context of emerging markets. This study provides the first examination of this anomaly in advanced emerging markets, which allows for a robust analysis of the pervasiveness and the drivers of this purported weekly pattern in stock returns given advanced emerging countries are spread around the world in the northern and southern hemispheres, as well as on the equator. For instance, the equator runs through Malaysia and this country does not adopt a system of daylight saving. In addition, South Africa is in the southern hemisphere and according to
SAD hypothesis, should experience this effect at a different time of the year than countries in the northern hemisphere. Consequently, the result of this study can test the efficacy of the SAD hypothesis as the main driver of the week of the year effect. Moreover, this study will be the first to test the weekly seasonality in the volatility of stock returns. Therefore, this thesis will test whether different weeks have different risks during the year.
The related hypothesis is as follow:
H5A: There are differences in the stock returns across weeks in
advanced emerging markets.
To conduct this study, time series regressions are used with dummy variables assigned to relevant weeks of the year. The result of this study shows that 44th week of the year has higher average returns in eight out of nine advanced
17 | Page emerging markets, with the exception of Malaysia. The robust and significant results in the other eight countries show that that 44th week has on average, seven times higher returns compared to the average for other weeks during the year.
Therefore, this seasonal pattern in returns appears to be robustly out-of-sample.
However, the results are not consistent with the existing explanations that have been put forward for this anomaly. Given evidence of a consistent pattern in returns is reported for countries located across different hemispheres, the results are not consistent with the SAD effect driving these returns, as seasonal changes in investor moods should be different for countries in different hemispheres.
H5B: There are differences in the volatility of stock returns across
weeks in advanced emerging markets.
The result of this study shows that weekly seasonality in volatility of stock returns exists in South Africa. Interestingly, stock returns during the 44th week of the year have higher volatility compared to other weeks of the year for this market.
Therefore, the 44th week of the year has higher risk and higher expected returns compared to other weeks in South Africa. This provides support for the argument that the higher volatility during the 44th week of the year causes investors to require a higher rate of return. In addition, this finding provides justification for the result on weekly return patterns across the South African stock market that appears to be at odds with the SAD explanation. Note that this study finds no significant seasonal pattern in volatility across other eight advanced emerging markets.
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1.2.2. Do advanced emerging markets exhibit the MAX effect?
Contrary to the notion that investors tend to hold perfectly diversified portfolios,
Mitton and Vorkink (2007) argue that many investors have a higher tendency to invest in stocks with positive skewness rather than completely diversifying away their unsystematic risk. Consequently, stocks with return distributions that are positively skewed may be overpriced in the market. This mispricing of stocks with lottery-like payoffs causes the anomalous returns called the MAX effect, which refers to the underperformance of stocks that have the highest daily returns over the previous calendar month. This anomaly was first documented by Bali,
Cakici, and Whitelaw (2011) in the US market. Studies also examined other developed markets and found strong evidence of a negative relationship between the magnitude of maximum daily stock returns and returns in the subsequent calendar month (Nartea et al., 2014; Walkshäusl, 2014).
Despite the prevalence of the MAX effect in developed markets, only a few studies have examined this anomaly in the context of emerging markets. The volatility characteristics in emerging markets are different when compared to more developed markets (Bekaert & Harvey, 1997) and these differences may affect investor preferences for idiosyncratic risk and hence the pervasiveness of the MAX effect. Moreover, emerging countries have different market characteristics compared to the US and other developed countries that affect the levels of mispricing and limits to arbitrage in these markets. For instance, arbitragers should take a short position in order to exploit the mispricing opportunity caused by over-priced stocks in the MAX portfolio. However, short
19 | Page selling is not accessible in emerging markets, which prevents this anomaly from being arbitraged away in these markets.
By examining the MAX effect in advanced emerging markets, this study contributes to the academic literature in the context of asset pricing in emerging markets. Given the MAX effect has been argued to be explained by investor mispricing rather than risk, this thesis will also contribute to the literature by providing an examination of the relationship between the returns on the MAX effect investment strategy and time-variation in the degree of limits to arbitrage in advanced emerging markets. The results from this chapter of the thesis can help to provide a better understanding of the underlying causes of the MAX effect and mispricing in emerging stock markets.
H6A: There is a negative relationship between the magnitude of
maximum daily returns and subsequent monthly returns in advanced
emerging markets.
This study performs the univariate and bivariate sorts, time series and panel regressions and finds strong evidence of a MAX effect in advanced emerging stock markets. The results are robust and persistent after controlling for firm size, book to market ratio, momentum, liquidity and market beta. In fact, the results show that the MAX effect is more prevalent in advanced emerging markets when compared to the US and other developed markets. This provides formative support for the contention that the mispricing related to the MAX effect might be more pervasive due to higher degree of limits to arbitrage in emerging markets
20 | Page relative to developed markets. This contention is directly tested through an additional hypothesis.
H6B: There is a relationship between the MAX effect and variables
that influence limits to arbitrage in advanced emerging stock markets.
Time series regressions are used to examine the relation between the MAX effect and dividend yield, transaction costs, volatility and liquidity. These variables have been shown to have a relation with the degree of limits to arbitrage in the stock market (Barberis & Thaler, 2003; Pontiff, 1996; Shleifer & Vishny, 1997). The results show that the MAX effect is statistically significantly related with at least one of the variables in eight out of nine advanced emerging markets. This result provides further evidence that the MAX effect can be attributed to mispricing caused by the preference for stocks with lottery type pay-offs, and it increases during the times when there are higher levels of limits to arbitrage in the market where rational investors are limited in their capacity to arbitrage away this mispricing.
1.3.Thesis outline
The remainder of this thesis proceeds as follows. Chapter 2 provides an in-depth analysis of the specific characteristics of advanced emerging markets that can affect returns and volatility patterns in the stock markets of these countries. This chapter outlines the important aspects of these markets, provides an overview of the regulatory and institutional framework and details of liberalization, discusses the relevant exchange rate regimes and provides an overview of the corporate
21 | Page governance specification of these markets. The chapter also provides a detailed explanation of market specifications as well as the return and volatility characteristics of stocks in advanced emerging countries. The chapter compares the specification of advanced emerging markets with the US and uses this comparison to provide an explanation for possible differences in the pricing of assets in advanced emerging markets compared with developed markets.
Chapter 3 provides the results from the analysis of the existence of seasonal anomalies in advanced emerging markets. More specifically, it examines the pervasiveness of five seasonal anomalies in advanced emerging markets that has been identified in developed markets: the January effect, the other January effect, the day of the week effect, the holiday effect and the week of the year effect. After examining the out-of-sample efficacy of seasonal anomalies, this study uses the unique characteristics of emerging markets to test the proposed explanations that have been attributed to the seasonal patterns in returns identified in developed markets.
Chapter 4 reports the results of the examination of the pervasiveness of the
MAX effect and the relationship between the returns on the MAX investment strategy and the degree of limits to arbitrage in advanced emerging markets. This chapter provides an overview of the returns generated by the investment strategy, after controlling for a range of other factors that have been shown to be related with returns, and then extends the existing literature by using four variables as proxies for limits to arbitrage and examining the time series relation between the
MAX effect and the degree of limits to arbitrage in advanced emerging markets.
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Chapter 5 provides an overview of the previous chapters and the motivation of this study. The chapter states the key findings of the thesis and discusses the implications of the results for academics, investors and policy makers. The chapter concludes with the limitations of this thesis and possible directions for future research.
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Chapter 2 : Overview of advanced emerging markets
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2.1.Introduction
Researchers and practitioners try to develop plausible models that can adequately explain the variation of stock returns. Different models have been identified and many anomalies have been introduced in the literature in the last few decades.
Studies suggest many propositions for the underlying reasons of these anomalies.
The characteristics of the markets under study are important drivers of anomalies.
For instance, the corporate governance characteristics in the country can affect the information asymmetry and consequently, the market efficiency in the country
(Claessens & Yurtoglu, 2013). Moreover, the fiscal year of the country (Branch,
1977), the weather (Hirshleifer & Shumway, 2003), location of the country
(Kamstra et al., 2003) and the dominant religion of market participants
(Białkowski, Etebari, & Wisniewski, 2012) can introduce seasonality in stock returns. Therefore, it is important to understand the characteristics of different markets around the world and recognize the differences between them. Based on the Financial Times Stock Exchange (FTSE, 2014) market classification criteria, stock markets around the world can be categorized as developed, advanced emerging, secondary emerging and frontier markets. This chapter studies the characteristics of advanced emerging markets that may be relevant to two of the most important anomalies discovered in stock returns: seasonality and the MAX effect.
Advanced emerging markets have been an appealing investment choice for investors since their liberalization in the late 1980s and early 1990s. There is a growing interest in developing countries for international investors who want to
26 | Page diversify away their country-specific risk. In fact, the emerging market share of global market capitalization experienced dramatic growth from less than 1% in
1987 to about 12.5% at the beginning of 2012, which shows that there is growing interest in emerging markets in the last two decades (Bekaert & Harvey, 2014).
This upward interest can have many reasons. First, low correlation between emerging and developed markets is one of the most important reasons that motivates investors to invest in emerging markets and benefit from diversification
(Harvey, 1995). Second, emerging markets often outperform developed markets because emerging economies are growing faster than developed economies.
Bekaert and Harvey (2003) examine emerging market portfolios of US investors and find that these portfolios have outperformed many important benchmarks.
According to FTSE (2014), there are nine countries in the category of advanced emerging markets: Brazil, Czech Republic, Hungary, Malaysia, Mexico,
Poland, South Africa, Taiwan and Turkey. Since their liberalization, advanced emerging markets have become more integrated with developed markets. It is important to note that integration is a gradual process, not achieved in a short period of time (Bekaert & Harvey, 2002). Although these markets have been liberalized, there are still barriers for international investors to invest in these markets (Bekaert & Harvey, 2002). These markets still have small legal barriers that make foreign investment difficult compared to developed markets. Moreover, the extra risk that foreign investors face is an indirect barrier for foreign investments. Many reasons contribute to create this extra risk in these markets such as minority investor protection, accounting standards and information asymmetry. There are also risks associated with the currency exchange rate,
27 | Page liquidity, political and economic policy (Bekaert & Harvey, 2002). Such extra risks cause unique returns and volatility patterns in advanced emerging markets that are different to developed markets (Bekaert & Harvey, 2014) because the higher risks impede the act of arbitragers and prevent any mispricing from being arbitraged away. Consequently, the extra risk provides a greater level of limit to arbitrage in these markets compared to developed markets (Switzer & Tahaoglu,
2014) and the mispricing will be more pervasive in advanced emerging markets.
Therefore, stocks’ returns and volatilities are expected to have different patterns and the anomalies to be more persistent in these markets.
It is the common belief regarding less developed markets that these markets still exhibit extra risk for foreign investors related to information asymmetry, liquidity, exchange rates, political and economic policy which can act as indirect barriers to foreign investment and limits to arbitrage in these markets
(Bekaert & Harvey, 2014). This extra risk and the higher limits to arbitrage lead to higher mispricing, higher anomalous returns and greater deviation from random walk in emerging markets. Therefore, it can be argued that emerging markets are less efficient compared to developed markets. However, contradictory to this notion, Griffin et al. (2010) argue that higher anomalous returns are not achievable because of higher transaction costs in emerging markets. They argue that emerging markets are just as efficient as developed markets and investors cannot make more profit compared to developed markets. They claim that common measures of market efficiency often show higher deviation from random walk in developed markets compared to their emerging market counterparts
(Griffin et al., 2010).
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Regardless of the level of market efficiency in emerging compared to developed countries, many studies show that emerging countries have unique market characteristics which can cause different return and volatility patterns and thus affect pervasiveness of anomalies in stock returns in their markets. Therefore, it is essential to study such unique market characteristics and examine the pervasiveness of anomalous returns in emerging countries.
The remainder of this chapter proceeds as follows. Section 2 explains the regulatory and institutional framework in advanced emerging markets. Section 3 discusses the characteristics of advanced emerging markets. Section 4 provides the return and volatility characteristics in advanced emerging markets. Section 5 discusses the geographical location of advanced emerging countries and explains how it might be relevant and Section 6 provides a summary.
2.2.Regulatory and institutional framework in emerging markets
2.2.1. Equity market liberalization
Government regulations can directly affect the characteristics of stock markets; thus, it is essential to study the regulatory framework of advanced emerging markets. Emerging markets’ regulations used to have more restrictions on foreign ownership of stocks and international capital flow and thus could prevent foreign investors from exploiting arbitrage opportunities in those domestic markets. Such limits to foreign arbitragers may result in more prevalent asset pricing anomalies.
Advanced emerging markets started to liberalize and ease restrictions on foreign
29 | Page investment in the late 1980s and early 1990s. The liberalization process caused these markets to become more integrated with the global market. These regulation changes are necessary to consider because they can affect the pervasiveness of stock return anomalies in these markets.
Financial liberalization allows funds to be transferred into and out of the country more easily (Bekaert & Harvey, 2003). This means that foreigners should be easily able to transfer funds into a country, buy financial securities, hold them for as long as they desire, sell them whenever they want and repatriate their capital and any profits without restrictions. On the other hand, domestic investors should be able to purchase and sell foreign securities without government intervention. Markets are considered fully integrated when assets with the same characteristics and identical risks have equal expected returns regardless of trading location (Bekaert & Harvey, 2003).
Table 2.1 shows the official liberalization dates of the advanced emerging markets. The official date of liberalization is the day in which the liberalization process started. Note that there was no relevant data available for the Czech
Republic. Malaysia was the first country to start the integration process by liberalizing foreign ownership policies to attract foreign investment in December
1988 and South Africa was the last country to start the integration process by lifting restrictions on foreign membership in the stock market in 1996.
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Table 2.1 Advanced emerging equity market liberalization dates Source: Bekaert, Harvey, and Lundblad (2003); Bae, Bailey, and Mao (2006); Kim and Kenny (2007) Official Countries Liberalization Notes Date Brazil 1991/05 Change of foreign investment law. Czech Republic - - Hungary 1994 Introduction of first ADR and first closed-end country fund. Malaysia 1988/12 Budget calls for liberalization of foreign ownership policies to attract more foreign investors. Mexico 1989/05 Restrictions on foreign capital participation in new direct foreign investment were liberalized substantially. Poland 1995 Introduction of first ADR and first closed-end country fund. South Africa 1996 Restrictions on foreign membership in the Johannesburg Stock Exchange lifted. Taiwan 1991/01 Eligible foreign institutional investors may now invest directly in Taiwan securities subject to approval. Turkey 1989/08 Foreign investors were permitted to trade in listed securities with no restrictions at all and pay no withholding or capital gains tax.
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If the domestic market is integrated with foreign markets, foreign investment would drive up the local security prices permanently and; therefore, reduce the cost of capital in the domestic market (Bekaert & Harvey, 2003).
Bekaert, Harvey, and Lundblad (2002) show that, while the ratio of consumption to GDP does not change after financial liberalization, the ratio of investment to
GDP increases. On the other hand, liberalization is conducive to financial market development, which can boost growth and economic well-being of the country
(Levine, Loayza, & Beck, 2000). For instance, foreign investors may demand for more accurate information disclosure, better corporate governance and accounting standards, which can further increase foreign investments (Bekaert & Harvey,
2003). Bekaert et al. (2002) examined 95 countries and state that, after taking into account other variables that might lift economic performance, the average annual per capita GDP growth improves by 0.7% to 1.4% after liberalization.
Although advanced emerging countries have been liberalized, there are still extra risks in advanced emerging markets, which act as an indirect barrier for foreign investors to transfer their funds to these markets. The extra risk in advanced emerging markets can also limit the arbitragers to exploit the mispricing opportunity that exists and cause stock return anomalies to be more pronounced in these markets.
Table 2.2 shows institutional and regulatory differences across developed, advanced emerging, secondary emerging and frontier markets based on the FTSE market classification criteria (FTSE, 2014). Since the liberalization of advanced emerging markets, their regulatory regime and market structure have improved and become closer to developed markets than secondary emerging markets. For
32 | Page instance, advanced emerging markets improved their regulatory environment in the area of corporate governance and many aspects of dealing landscape and custody procedures. More specifically, they now have better minority shareholder protection and well-developed equity and foreign exchange markets. Moreover, they have relaxed restrictions on foreign ownership and made the ownership registration process easier for foreign investors (FTSE, 2014), which will help to attract more foreign investments. However, there are still market frictions in advanced emerging markets that can result in return anomalies. For instance, stock lending is not permitted and free delivery settlement is not available in these markets. Moreover, there is limited scope of short selling and off-exchange transactions in advanced emerging markets. Trading mechanisms are also less efficient in these markets compared to developed markets.
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Table 2.2: Characteristics of equity markets based on the categorization of the Financial Times Stock Exchange (FTSE, 2014)
The table shows the quality of the markets in different country classifications.
Markets Developed Advanced Emerging Secondary Emerging Frontier Market and Regulatory Environment Formal stock market regulatory authorities actively monitor market (e.g., SEC, FSA, SFC) Fair and non-prejudicial treatment of minority shareholders
Non or selective incidence of foreign ownership restrictions
No objection to or significant restrictions or penalties applied to the investment of capital or the repatriation of capital and income Free and well-developed equity market
Free and well-developed foreign exchange market
Non or simple registration process for foreign investors
Custody and Settlement Settlement - Rare incidence of failed trades Custody-Sufficient competition to ensure high quality custodian services
Clearing & settlement - T+3 or shorter, T + 5 or shorter for Frontier Stock Lending is permitted
Settlement - Free delivery available
Custody - Omnibus account facilities available to international investors
Dealing Landscape Brokerage - Sufficient competition to ensure high quality broker services
Liquidity - Sufficient broad market liquidity to support sizeable global investment
Transaction costs - implicit and explicit costs to be reasonable and competitive
Short sales permitted
Off-exchange transactions permitted
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Markets Developed Advanced Emerging Secondary Emerging Frontier Efficient trading mechanism
Transparency - market depth information / visibility and timely trade reporting process Derivatives Developed Derivatives Market
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Table 2.3 shows the country-specific information on stock market regulatory environment for the advanced emerging markets and the US provided by the FTSE (2014). The table shows that the advanced emerging markets are improving their regulatory environments and becoming closer to more developed markets. For instance, South Africa passes all criteria of the FTSE. However, there are still few aspects in these markets that are different compared to the US, and which can cause the stock return patterns to be different in these markets compared to the US. For instance, stock lending is not permitted the Czech
Republic, Hungary, Malaysia and Taiwan and short selling is restricted in the
Czech Republic, Malaysia, Mexico and Taiwan. This is particularly important for anomalies that involve overvalued stocks, because the act of arbitraging away from overvalued stocks requires investors to short-sell the stocks to be able to make abnormal returns, is restricted. Therefore, such mispricing will be more prevalent in these markets and the short-selling/stock lending restriction will act as limits to arbitrage. It is worth considering that the existence of such limit to arbitrage may cause the MAX anomaly to be significantly different in these markets because a high proportion of abnormal return from this anomaly is coming from the negative returns of the high MAX portfolio stocks, which are most overvalued (Bali et al., 2011). Consequently, arbitragers cannot short sell these stocks and correct the mispricing in these markets. Table 2.3 also shows that the foreign exchange market is restricted in Brazil, Malaysia and Taiwan and there are some restrictions on the foreign ownership in Malaysia and Mexico. These regulations can impose limits and add extra risk for foreign investments, which
36 | Page can cause further limit to arbitrage in these markets. Consequently, this will affect the anomalous return patterns in these markets.
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Table 2.3: Country level regulatory environment and market characteristics
Markets Brazil Czech Republic Hungary Malaysia Mexico Poland South Africa Taiwan Turkey US
Market and Regulatory Environment Formal stock market regulatory authorities actively monitor market (e.g., SEC, FSA, SFC) Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Fair and non-prejudicial treatment of minority shareholders Restricted Pass Pass Pass Pass Pass Pass Pass Pass Pass Non or selective incidence of foreign ownership restrictions Pass Pass Pass Restricted Restricted Pass Pass Pass Pass Pass
No objection to or significant restrictions or penalties applied to the investment of capital Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass or the repatriation of capital and income
Free and well-developed equity market Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Free and well-developed foreign exchange market Restricted Pass Pass Restricted Pass Pass Pass Restricted Pass Pass Non or simple registration process for foreign investors Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
Custody and Settlement Settlement - Rare incidence of failed trades Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Custody-Sufficient competition to ensure high quality custodian services Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Clearing & settlement - T+3 or shorter, T + 5 or shorter for Frontier T+3 T+2 T+2 T+3 T+3 T+2 T+5 Restricted T+2 T+3 Stock Lending is permitted Pass Restricted Restricted Restricted Pass Pass Pass Restricted Pass Pass Settlement - Free delivery available Pass Pass Pass Pass Pass Pass Pass Restricted Pass Pass Custody - Omnibus account facilities available to international investors Pass Pass Pass Pass Pass Restricted Pass Pass Restricted Pass
Dealing Landscape Brokerage - Sufficient competition to ensure high quality broker services Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Liquidity - Sufficient broad market liquidity to support sizeable global investment Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Transaction costs - implicit and explicit costs to be reasonable and competitive Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass Short sales permitted Pass Restricted Pass Restricted Restricted Pass Pass Restricted Pass Pass Off-exchange transactions permitted Not Met Pass Pass Pass Pass Pass Pass Restricted Pass Pass Efficient trading mechanism Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
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Transparency - market depth information / visibility and timely trade reporting process Pass Pass Pass Pass Pass Pass Pass Pass Pass Pass
Derivatives
Developed Derivatives Market Pass Not Met Pass Restricted Pass Pass Pass Pass Restricted Pass
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2.2.2. Exchange rate regime
The exchange rate regime is one of the most important issues that foreign investors should consider before investing in the stock market of other countries.
Foreign exchange risk can arise from excess exchange rate volatility, but an additional source of foreign exchange risk in the context of an emerging market can be currency convertibility risk. Foreign exchange risk and lack of hedging instruments may prevent foreign arbitragers to invest in advanced emerging markets and thus, act as a limit to arbitrage. This limit for foreign arbitragers may cause anomalous returns to be more prevalent. Therefore, it is important to examine characteristics of foreign exchange markets.
Five out of the nine advanced emerging markets, which used a floating exchange rate regime for the entire sample period, are the Czech Republic,
Hungary, Poland, South Africa and Taiwan. Although these countries did not significantly change the regulations related to the exchange rates, they experienced high exchange rate fluctuations. For instance, the South African Rand experienced high fluctuations and depreciated by more than 77% from 0.2822 US dollar on 2nd January 1995 to 0.0636 US dollar on 31st December 2015. Figure 2.1 shows the value of a USD invested in South African Rand at the beginning of
1995 over these 20 years. These high fluctuations and the depreciation of the
South African Rand add extra risk to international investors who are willing to invest in the South African stock market.
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Figure 2.1: The value of one US dollar invested in the South African Rand 1 0.9 0.8
0.7 0.6 0.5 0.4
Value Rand of Value 0.3 0.2 0.1 0
Date
On the other hand, the other four advanced emerging markets experienced important changes in their currency exchange rate regime, which is necessary to consider. Below is a brief explanation of these changes of regulatory environment for these countries. The Brazilian Real was first introduced in September 1994 with the value of one US dollar. Figure 2.2 shows the change in the value of
Brazilian Real in US dollar terms over time for the entire sample period used in this study. The currency had an unexpected appreciation and was worth 1.2 US dollars due to the large capital inflows in late 1994 and 1995. This was the time when the Central Bank started to intervene in order to control the exchange rate and smoothly depreciated the currency to 0.83 US dollar at the end of 1998. It is clear in the figure that there was minimal fluctuation in the exchange rate during this period and the Central Bank was able to smoothly depreciate the currency. In
1999, international markets deteriorated because of the Russian default, which forced the Central Bank to float the exchange rate and the Real started to depreciate (Pinheiro, Giambiagi, & Moreira, 2001). The Central Bank has kept the
41 | Page exchange rate floating since then, and has been able to maintain stability in the exchange market except for the 2002 currency crisis when the exchange rate depreciated to the historically low value of 0.25 US dollar.
Figure 2.2: The value of the Brazilian Real currency per US dollar 1.4 1.2
1 0.8 0.6
Value Real of Value 0.4 0.2 0
Date
Figure 2.3 shows the value of the Malaysian Ringgit over the entire sample period. The currency was free floating until the 1997 Asian financial crisis when it depreciated by about 50% to 0.25 US dollar. In 1998, the Central Bank pegged the currency to the US dollar for seven years. The figure clearly shows no fluctuation in the exchange rate during this period. In July 2005, the Central Bank changed the regulation and started to use the managed floating exchange rate regime.
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Figure 2.3: The value of the Malaysian Ringgit currency per US dollar 0.45 0.4 0.35 0.3 0.25 0.2 0.15
Value Ringgit of Value 0.1 0.05 0
Date
Mexico moved from a pegged exchange rate regime to a floating exchange rate regime in December 1994. This move was followed by substantial depreciation of the Mexican Peso in 1995 known as the ‘Mexican Peso Crisis’
(Carstens & Werner, 2000). On the other hand, the exchange rate regime was free floating in Turkey during the hyperinflation in the late 1990s and early 2000s.
This caused the currency to depreciate. Figure 2.4 shows this dramatic depreciation from the US dollar perspective. In January 2005, Turkey had one of the least valued currencies in the world when the Central Bank revaluated the currency and introduced the new Lira, removing six zeros from the old Lira.
Turkey continues to have free-floating exchange rate regime.
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Figure 2.4: The value of the Turkish Lira per US dollar Panel A: From 1995 to 2016 35 30 25 20 15 10 5 0
Panel B: From 2001 to 2016 1.6 1.4 1.2 1 0.8 0.6 0.4 0.2 0
The fact that many advanced emerging markets have experienced significant exchange rate regime changes and currency fluctuations and depreciations in the recent years, imposes excessive risk and acts as an indirect barrier for investors and arbitragers. On the other hand, hedging instruments for foreign exchange risk are mostly not available to investors who want to mitigate the risks associated with the currency of advanced emerging markets. All of these factors may act as limits to arbitrage in these markets.
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In the absence of foreign arbitragers, these markets will work less efficiently compared to more developed markets (Bekaert & Harvey, 2014).
Therefore, the levels of mispricing will be higher in advanced emerging markets and anomalies may be more pronounced in the stock returns of these markets. As empirical application of such lower efficiency in advanced emerging markets, the calendar effects and the MAX effect are expected to be more pronounced and the mispricing in these markets to remain longer. Therefore, seasonal return patterns and the MAX anomaly may differ in advanced emerging markets compared to the
US market.
2.2.3. Social institutions and culture
Social institutions and culture can affect stock returns and volatility characteristics of the stock market. The official holidays in advanced emerging markets differ significantly from developed markets because of cultural and religious differences. This is particularly relevant for studying seasonality in these stock markets. More precisely, the characteristics of holidays as well as the number of national holidays are different and that may affect the pervasiveness of the holiday effect in these markets. Table 2.4 shows the list of national holidays in each of advanced emerging markets for 2014. Note that a few national holidays do not have a set date every year and are floating. For instance, the Malaysian Islamic holiday moves around in the normal calendar year because Malaysia follows a different type of calendar. The number of national holidays ranges between ten days for Mexico and twenty days for Malaysia during the year. Religious and cultural events are more prevalent in multi-ethnic countries like Malaysia. Most of
45 | Page the advanced emerging markets have Easter, Christmas and New Year’s Eve and
Labour Day as their holidays. Taiwan and Malaysia have also holidays related to the Chinese New Year and Malaysia and Turkey have holidays related to
Ramadan. These holidays have been crosschecked against data obtained from
Datastream to ensure that there is zero trading volume on these reported holidays.
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Table 2.4: Stock exchange holidays in advanced emerging markets This table reports the name and date of all public holidays in the sample of advanced emerging markets examined in this study for the year 2014. The data used to construct these tables is cross checked against Datastream trading volume data and is sourced from: • BM&FBOVESPA (2014) • Budapest Stock Exchange (2014) • Bursa Malaysia (2014) • Bursa Istanbul (2014) • Holiday Tracker (2014) • International Monetary Fund (2014) • Johannesburg Stock Exchange (2014) • Stock Market Holidays (2014) • Taiwan Stock Exchange (2014) • Warsaw Stock Exchange (2014) Date Holidays Date Holidays Brazil 1-Jan New Year's Day 19-Jun Corpus Christi Day The Constitutional Battle of 3-Mar Carnival 9-Jul 1932 (state holiday) 4-Mar Carnival 20-Nov Black Consciousness Day 18-Apr Good Friday 24-Dec Christmas Eve 21-Apr Tiradentes 25-Dec Christmas Day 1-May Labour Day 31-Dec New Year's Eve
Czech Republic Freedom and Democracy 1-Jan New Year’s Day 17-Nov Day 18-Apr Good Friday 24-Dec Christmas Eve 21-Apr Easter 25-Dec Christmas Day 1-May May Day 26-Dec St. Stephen’s Day 8-May Liberation from Fascism 31-Dec New Year’s Eve Establishment of the 28-Oct Czechoslovak Republic
Hungary 1-Jan New Year's Day 20-Aug National Day 18-Apr Good Friday 23-Oct National Day 21-Apr Easter Monday 24-Oct Public Holiday 1-May Labor Day 24-Dec Christmas Eve 2-May Public Holiday 25-Dec Christmas Day 9-Jun Whit Monday 26-Dec Boxing Day 31-Dec New Year’s Eve
Malaysia 1-Jan New Year's Day 7-Jun King's Birthday Birthday of Prophet 14-Jan 15-Jul Nuzul Al‐Quran Muhammad 17-Jan Thaipusam 28-Jul Hari Raya Puasa (Eid-ul-
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Fitri) Hari Raya Puasa (Eid-ul- 30-Jan Chinese New Year Eve 29-Jul Fitri) 31-Jan Chinese New Year 31-Aug National Day 1-Feb Federal Territory Day 16-Sep Malaysia Day Hari Raya Haji (Eid-ul- 2-Feb Chinese New Year 5-Oct Adha) 3-Feb Chinese New Year 22-Oct Deepavali Awal Muharram (Maal 1-May Workers' Day 25-Oct Hijrah) 13-May Wesak Day 25-Dec Christmas Day
Mexico 1-Jan New Year's Day 1-May Labour Day 4-Feb Constitution Day 16-Sep Independence Day 18-Mar Juarez’s Birthday 18-Nov Mexican Revolution 28-Mar Holy Thursday 12-Dec Our Lady of Guadalupe 29-Mar Good Friday 25-Dec Christmas Day
Poland Assumption of the Blessed 1-Jan New Year's Day 15-Aug Virgin Mary 6-Jan Twelfth Day 11-Nov Independence Day 18-Apr Easter Sunday 24-Dec Christmas Eve 21-Apr Easter Monday 25-Dec Christmas Day 1-May Labour Day 26-Dec St. Stephen’s Day
19-Jun Corpus Christi 31-Dec New Year's Eve
South Africa 1-Jan New Year's Day 1-May Workers' Day 21-Mar Human Rights Day 16-Jun Youth Day 18-Apr Good Friday 9-Aug National Women's Day 21-Apr Family Day 24-Sep Heritage Day 27-Apr Freedom Day 16-Dec Day of Reconciliation 28-Apr Public Holiday 25-Dec Christmas Day 26-Dec Day of Goodwill
Taiwan 1-Jan New Year’s Day 3-Apr Adjusted Holiday 2-Jan Adjusted Holiday 4-Apr Children's Day 16-Feb No Trading 5-Apr Tomb-sweeping Day Market opens only for 17-Feb 6-Apr Adjusted Holiday Clearing & Settlement. 18-Feb Lunar New Year's Eve 1-May Labor Day 19-Feb Spring Festival 19-Jun Adjusted Holiday 20-Feb Spring Festival 20-Jun Dragon Boat Festival 21-Feb Spring Festival 27-Sep Mid-autumn Festival 22-Feb Spring Festival 28-Sep Adjusted Holiday
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23-Feb Spring Festival 9-Oct Adjusted Holiday 27-Feb Adjusted Holiday 10-Oct National Day 28-Feb Peace Memorial Day
Turkey 1-Jan New Year Holiday 30-Aug Victory Day National Sovereignty and 23-Apr 3-Oct Sacrifice Feast (Half Day) Children's Day 1-May Labour and Solidarity Day 4-Oct Sacrifice Feast Commemoration of Atatürk, 19-May 5-Oct Sacrifice Feast Youth and Sports Day 27-Jul Ramadan Feast 6-Oct Sacrifice Feast 28-Jul Ramadan Feast 7-Oct Sacrifice Feast 29-Jul Ramadan Feast 28-Oct Republic Day (Half Day) 30-Jul Ramadan Feast 29-Oct Republic Day
The religion of the majority of the population can also affect the behaviour of market participants in the country, which can directly affect the stock returns and prevalence of stock return anomalies. For instance, Białkowski et al. (2012) suggest that the stock returns during the month of Ramadan are significantly higher and less volatile in the 14 Muslim countries. During the month of
Ramadan, the psychology of investors changes and that leads to different behavioural patterns in the stock markets of Muslim countries (Białkowski et al.,
2012). Therefore, different religions in advanced emerging countries can introduce unique seasonal patterns in the stock returns of the country. Among nine advanced emerging markets, Islam is the dominant religion of Malaysia and
Turkey, Buddhism is the dominant religion in Taiwan and Christianity is the dominant religion in Brazil, Hungary, Mexico and Poland. Note that the population of South Africa and the Czech Republic do not have a dominant religion (Central Intelligence Agency, 2016).
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2.2.4. Corporate governance
The corporate governance environment can affect a country’s stock market and the behaviour of market participants. It is now a well-known fact that better corporate governance can mitigate information asymmetry in the stock market and lead to a more efficient stock market (Claessens & Yurtoglu, 2013). A strong corporate governance environment, and the mitigation of information asymmetry in the market, encourages arbitragers to exploit any mispricing opportunity.
Therefore, better corporate governance may result in scarce and weaker mispricing and thus, weaker anomalous returns. Studies show that a corporate governance environment affects stock returns and volatility in the market (Bae,
Baek, Kang, & Liu, 2012; Cornett, McNutt, & Tehranian, 2009). Therefore, it is necessary and essential to study the corporate governance environment of advanced emerging markets. The first step is to define the exact meaning of corporate governance. From the perspective of any stock market, corporate governance refers to the way outside investors can protect themselves against expropriation by the insiders and can be measured by different variables such as legal rights’ strengths, anti-corruption laws and disclosure requirements
(Claessens & Yurtoglu, 2013).
Better investor protection and corporate governance lead to a more efficient and transparent financial markets (Claessens & Yurtoglu, 2013). For instance, stocks with more investor protection trade at a lower bid-ask spread
(Brockman & Chung, 2003). Consequently, better investor protection can reduce the transaction cost and increase liquidity in the stock market. This can
50 | Page significantly reduce the risk of investors and hence, change the return and volatility characteristics of stocks.
Better corporate governance can also improve the performance of the firms in different ways, which leads to more efficient stock markets. For instance, better corporate governance improves the relationship with the stakeholders, and reduces the risks for investors. Consequently, firms will have easier and cheaper access to external financing, which will lead to greater and more efficient investment (Claessens & Yurtoglu, 2013). In fact, a better legal environment would not only increase the financing able to be obtained by the firms, but also help firms to invest in intangible assets more efficiently (Claessens & Laeven,
2003). Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2008) create the measure of anti-self-dealing as a proxy for legal protection of minority shareholders and argue that higher anti-self-dealing increases the number of domestic firms and initial public offerings. Chen, Chen, and Wei (2011) claim that, after controlling for other risk factors, US firms with better corporate governance have a lower cost of capital. They also studied firm-level corporate governance and found that it reduced the cost of equity in 17 emerging markets
(Chen et al., 2011).
Better corporate governance can also reduce a firm’s downside risks. Lel
(2012) provides evidence that firms with better corporate governance are more likely to hedge their risks. Bae, Kang, and Wang (2011) claim that firms that have moral treaties with their employees are more likely to maintain lower debt ratios and have less risk of financing. Bae et al. (2012) show that firms with weaker corporate governance experienced a greater drop in share price during the Asian
51 | Page crisis in 1997 and argue that better corporate governance will lead to less volatility of the share price. Similarly, Cornett et al. (2009) found that firms with better internal corporate governance had higher returns during the crisis.
Therefore, the corporate governance environment and regulations affect the characteristics of risks and returns in the stock market of the country. The higher returns and lower volatility of stock prices are particularly important for examining the MAX effect in advanced emerging markets because stocks with better corporate governance will have different return distributions. For instance, this may also affect the skewness and kurtosis, and cause fewer extreme returns, which directly affect the pervasiveness of the MAX effect in the markets. Hence, it is necessary to study the corporate governance regulatory environment before studying the anomalous returns in advanced emerging markets.
The government is the official regulatory authority, which sets the minimum disclosure requirements for the companies listed on a country’s stock market. These disclosure requirements, and their standards, are the most important tools for investors to use to understand the overall business as well as the detailed financial status of the company and; hence, understand the risk of their investments. Table 2.5 shows the disclosure requirements in advanced emerging markets versus the US. The disclosure items are based on the International
Standards of Accounting and Reporting (ISAR) benchmark and the information on individual countries has been gathered by the United Nations (2011). Based on
ISAR, 52 disclosure items should be disclosed in order for a market to be ranked as having better corporate governance transparency. However, the table shows that many necessary disclosure items are not required in advanced emerging
52 | Page markets. The number of required disclosure item ranges from 25 in Turkey to 52 in South Africa, while in the US, firms are required to provide disclosures for 50 out of the 52 disclosure items. One example of the difference in disclosure between advanced emerging markets and the US is that in the Czech Republic,
Poland and Turkey, firms are not required to disclose information regarding corporate responsibility. Therefore, firms in these markets may elect not to invest in improving their corporate governance regime.
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Table 2.5: Disclosure items of International Standard of Accounting and Reporting (ISAR) benchmark from the United Nation Conference on Trade and Development (UNCTAD). The crossed items show that disclosure of the related item is not required in the related country.
South Brazil Czech Hungar Malaysia Mexico Poland Taiwan Turkey US Africa No. Disclosure Item (45) Republic (27) y (47) (46) (31) (43) (41) (25) (50) (52)
1 Ownership structure 2 Process for holding annual general meetings
3 Changes in shareholdings 4 Control structure structure 5 Control and corresponding equity stake 6 Availability and accessibility of meeting agenda 7 Control rights Ownership 8 Rules governing the acquisition of corporate control 9 Anti-takeover measures 10 Financial and operating results
11 Critical accounting estimates 12 Nature, type and elements of related-party transactions 13 Company objectives 14 Impact of alternative accounting decisions 15 The decision-making process for approving transactions 16 Rules and procedures governing extraordinary transactions Financial Transparency 17 Board’s responsibilities regarding financial communications
18 Process for interaction with internal auditors g 19 Process for interaction with external auditors Auditin
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20 Process for appointment of external auditors 21 Process for appointment of internal auditors Board confidence in independence and integrity of external 22 auditors 23 Internal control systems 24 Duration of current auditors 25 Rotation of audit partners Auditors` involvement in non-audit work and the fees paid to the 26 auditors 27 Policy in connection with environmental and social responsibility
Impact of environmental and social responsibility policies on the 28 firm’s sustainability 29 A code of ethics for the board and waivers to the ethics code 30 A code of ethics for all company employees 31 Policy on "whistle blower" protection for all employees 32 Mechanisms protecting the rights of other stakeholders in business 33 The role of employees in corporate governance Corporate Responsibility
Governance structures to prevent conflict of interest 35 Checks and balances mechanisms Composition of board of directors (executives and non- 36 executives) 37 Composition and function of governance committee structures 38 Role and functions of the board of directors 39 Risk management objectives, system and activities 40 Qualifications and biographical information on board members 41 Types and duties of outside board and management positions Board and Management Structure Management and Board 42 Material interests of members of the board and management
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43 Existence of plan of succession 44 Duration of director’s contracts Compensation policy for executives departing the firm as a result 45 of a merger or acquisition 46 Determination and composition of directors` remuneration 47 Independence of the board of directors Number of outside board and management position directorships 48 held by the directors Procedure(s) for addressing conflicts of interest among board 49 members 50 Professional development and training activities 51 Availability and use of advisorship facility during reporting period 52 Performance evaluation process
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These items can have a direct effect on the stocks, and change the return patterns related to seasonality and the MAX effect. For instance, the financial transparency and rules governing extraordinary transactions should be clear to investors. The absence of such items may lead to errors of judgement in the stock market and create mispricing. Such mispricing will affect returns and volatility patterns in stock markets.
Table 2.6 shows the corporate governance environment in advanced emerging markets gathered from Claessens and Yurtoglu (2013). The first column of the table shows the origin of the law in advanced emerging countries versus the
US. It should be noted that the legal system of the countries mostly originates from the Civil Law (French and German origin) and the Common Law (British origin). In general, Common Law is considered to have better property rights protection (Claessens & Yurtoglu, 2013). Among advanced emerging countries,
South Africa and Malaysia have laws of British legal origin and the other seven countries’ laws have Civil Law origin. The table also shows detailed scores of advanced emerging countries in the strength of legal rights protecting shareholders, the market policies that prevent corruption in the stock market, the disclosure standards that companies are required to provide to shareholders, and the efficiency of debt enforcements. It is clear in the table that the US generally has higher scores compared to the advanced emerging countries’ average. For instance, the anti-corruption score is 152 in the US, while the average for advanced emerging countries is 23.8 with Taiwan having the highest score of 67.
However, a few countries among advanced emerging countries have improved in individual areas and they have higher scores compared to the US. For instance,
57 | Page the efficiency of debt enforcement score is 93.8 in Taiwan while the US has the score of 85.8. Similarly, the score related to legal protection of minority shareholders is 95 and 81 for Malaysia and South Africa respectively, while the
US has a score of 65. This shows that although advanced emerging countries generally have a weaker corporate governance environment, compared to developed markets, these countries are on the right path to improve their stock markets in this area.
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Table 2.6: The corporate governance environment in advanced emerging markets versus the US Legal rights strength refers to the strength of legal right index with the 10 being the strongest and 0 being the weakest 1 Taken from the The World Bank (2015). The legal protection of minority shareholders shows the anti-self-dealing index with 100 being the strongest and 0 being the weakest. Taken from the Porta, Lopez-de-Silanes, and Shleifer (2006). The anti-corruption is the average corruption score in which higher score means less corrupted Taken from the The World Bank (2015). Disclosure requirement and efficiency of debt enforcement show the index in which 0 is the weakest and 100 is the strongest score 1 Taken from the Porta et al. (2006 ) and Taken from the Shleifer, McLiesh, Hart, and Djankov (2008) respectively. Creditor right is on the scale of 0 to 4 and 4 is the strongest Taken from the Djankov, McLiesh, and Shleifer (2007). The corporate governance transparency index shows the score of the country based on corporates behaviours, how they provide their disclosure documents and financial statements Taken from the Claessens and Yurtoglu (2013).
Corporate Legal Rights Legal protection of Anti- Disclosure Creditors’ Efficiency of Country Legal origin governance strength minority shareholders corruption requirements rights debt enforcement opacity
Brazil French 3 29 -3 25 1 13.4 10 Czech Republic German 6.7 34 37 . 3 40.7 18 Hungary German 7 20 55 . 1 46.7 2 Malaysia British 10 95 28 92 3 48.4 9 Mexico French 5 18 -28 58 0 72.6 6 Poland German 8.2 30 36 . 1 67.7 11 South Africa British 9 81 40 83 3 39.8 16 Taiwan German . 56 67 75 2 93.8 0 Turkey French 4 43 -18 50 2 6.6 11 Average - 6.6 45.1 23.8 63.8 1.8 47.7 9.2 US British 8 65 152 100 1 85.8 21
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The lower average scores of advanced emerging countries in corporate governance compared to the US present extra risk to investors. This extra risk can cause investors to have higher expected returns to compensate for this and can act as limits to arbitragers. In addition, weaker corporate governance causes riskier performance from individual firms, leading to higher volatility of returns in these markets. Therefore, both return and volatility characteristics of stocks differ in advanced emerging countries compared to developed markets. Higher volatility may cause more mispricing. Higher levels of mispricing and limits to arbitrage can strengthen the pervasiveness of stock return anomalies in these markets. This is more important in the case of seasonality and the MAX effect anomalies because they are directly related to return and volatility characteristics of stocks.
2.2.5. Fiscal year
The fiscal year may differ from the calendar year and is relevant for studying monthly seasonality in stock returns. For instance, Branch (1977) argues that investors tend to sell their losing stocks before the end of the tax year and buy stocks after the start of the tax year to realize the occurred loss and reduce the amount of tax that should be payable to the government. Therefore, according to this hypothesis, there should be an increase in the volume of stock available for sale at the end of the tax year, which leads to the underperformance of the stock market. Similarly, there should also be an increase in demand for stock after the start of the tax year, which may lead to abnormal returns (Branch, 1977).
Consistent with this hypothesis, many studies find seasonal patterns in monthly returns of stocks in the US and many other developed markets (Reinganum, 1983;
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Rozeff & Kinney, 1976). More specifically, they find higher average returns in the month of January compared to other months of the year.
The fiscal year in eight out of nine advanced emerging markets is similar to the US, which is from January to December. South Africa is an exception, having a fiscal year from April to March. In addition, in all advanced emerging markets, local income is taxed and foreign income of non-residents is not taxed.
Moreover, in eight out of the nine, the foreign income of residents is taxed, with the exception of Malaysia. It is also important to study the tax rate on capital gains in each of the advanced emerging markets. The capital gains tax in Taiwan is
17%, in South Africa is 18.648%, in Poland and the Czech Republic is 19%, in
Turkey is 20%, and in Malaysia and Mexico is 30%. The capital gains tax in
Hungary and Brazil is not flat and ranges between 10% to 19% and 15% to 22.5% respectively (EY, 2016).
2.3.Market characteristics
Economic and financial characteristics of advanced emerging markets differ from their developed counterparts, and such differences can cause different stock return patterns. For instance, while the gross domestic product (GDP), in countries with advanced emerging markets, has grown in the past three decades, these emerging markets have failed to converge and are still relatively low compared to the US and other developed markets (Bekaert & Harvey, 2014). Hungary and Brazil have the minimum and the maximum GDP among advanced emerging markets, which were 137 and 2,347 billion US dollars, respectively, in 2015. The average GDP of
61 | Page advanced emerging countries is 752 billion US dollars while the GDP for the US market is 17,419 billion US dollars. The difference is still high when the per capita figure is considered. Among advanced emerging countries, South Africa
(Taiwan) has the lowest (highest) GDP per capita which is 6,483 (22,600) US dollars and the average GDP per capita in advanced emerging markets is 13,409
US dollars. However, the GDP per capita in the US is 54,629 US dollars in 2015, more than four times higher than the advanced emerging markets’ average. Hence, there is still big gap between advanced emerging markets and more developed markets such as the US. On the other hand, comparing the GDP growth rate of advanced emerging countries and the US gives a better indication of performance in their financial markets. According to the International Monetary Fund (IMF)
(2014), the GDP growth rate in advanced emerging markets ranges between
0.15% and 5.99% for Brazil and Malaysia respectively with an average of 2.83% in 2014 while the GDP growth rate is 2.43% in the US. In fact, six out of nine advanced emerging markets have a higher GDP growth rate than the US.
According to the IMF's (2014) forecast for the year 2020, this over-performance of advanced emerging markets over the US market will not only continue, but also become more prevalent. In 2020, the GDP growth rate in advanced emerging markets will be higher in all nine advanced emerging markets compared the US, ranging between 2.1% and 5% for Hungary and Malaysia respectively with the average of 3.11% while the US market will have a GDP growth rate of 1.96%
(IMF, 2014). This means that the overall financial markets in advanced emerging countries are expected to over-perform the US markets and investors can benefit
62 | Page from investing in these markets. Table 2.7 shows the detailed information regarding the GDP in advanced emerging markets versus the US.
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Table 2.7: Country level information and market characteristics of advanced emerging stock market The data related to the GDP at market price, GDP per capital, number of listed companies per 1,000,000 people and turnover ratio has been gathered from The World Bank (2015). The GDP growth rate and the prediction has been gathered from the International Monetary Fund (2014). The data related to market value has been gathered from Datastream and the number of listed companies has been gathered from FTSE (2015).
Advanced Czech South Brazil Hungary Malaysia Mexico Poland Taiwan Turkey Emerging US Republic Africa Average GDP at market prices (current Billion $2,346.6 $ 205.3 $ 137.0 $ 338.1 $1,291.1 $ 547.9 $ 350.1 $ 529,6 $ 798.3 $751.8 $17,348.1 US$) (2015) GDP per capita (current US$) (2015) $11,573 $19,526 $13,870 $11,049 $10,784 $14,411 $ 6,483 $22,600 $10,381 $13,408 $54,370 GDP growth rate (2014) 0.15% 1.98% 3.58% 5.99% 2.14% 3.44% 1.53% 3.77% 2.91% 2.83% 2.43% GDP growth rate (2020 predicted) 2.53% 2.18% 2.10% 5.00% 3.34% 3.60% 2.60% 3.16% 3.49% 3.11% 1.96% Market value (Billion USD) (1995) $ 78.0 $ 6.3 $ 1.4 $ 117.6 $ 76.7 $ 2.7 $ 114.9 $148.6 $ 14.8 $ 62.3 $3,401.2 Market value (Billion USD) (2015) $ 744.2 $ 28.6 $ 14.6 $ 384.0 $ 443.7 $ 142.9 $ 447.7 $620.7 $ 218.1 $ 338.3 $22,521.1 Average annual market value growth rate 11.34% 7.47% 11.81% 5.80% 8.72% 20.80% 6.69% 7.04% 13.67% 10.37% 9.42% (1995-2015) Number of listed companies (2015) 351 13 48 895 141 872 322 813 226 409 4369 Number of listed companies per 1.8 1.6 5.1 31.5 1.1 21.9 6.6 - 5.5 9.4 13.1 1,000,000 people (2012) Turnover Ratio (2015) 85.6 - 42.1 29.1 25.8 38.2 31.8 - 185.2 62.5 165.1
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On the other hand, despite the liberalization of advanced emerging markets since 1980s, their market capitalizations are still lower compared to developed markets (Bekaert & Harvey, 2014). The total market capitalization of the Czech Republic, Hungary and Poland were only 6.3, 1.4 and 2.7 billion US dollars respectively in 1995. The average market capitalization of advanced emerging markets was 62.3 billion US dollars in 1995 with 148.6 billion US dollars for Taiwan being the highest. Even Taiwan’s market capitalization is relatively low when it is compared to the total market capitalization of the US market, which was 3,401.2 billion US dollars in 1995. After the liberalization, these markets started to attract foreign investments and increased their market capitalization dramatically. At the end of the sample period in 2015, the average capitalization of advanced emerging markets was 338.3 billion US dollars, which shows a 543% increase in 20 years. The average annual growth rate of market capitalization for nine advanced emerging markets is 10.37%, higher than 9.42% for the US market. This means that the market value of advanced emerging countries is growing faster than the US market on average. However, despite this increase, the market capitalization of these markets is still lower when compared to the US and other developed markets. The US market capitalization was
22,521.1 billion US dollars in 2015, more than 66 times higher than advanced emerging markets’ average.
In addition, the number of stocks listed in the advanced emerging stock markets are low compared to the US and other developed markets. For instance, the number of stocks listed in the Czech Republic and Malaysia is 13 and 895 respectively; the minimum and the maximum in the range, and the average
65 | Page number for listed stocks in advanced emerging markets is 409. However, the number of stocks listed in the US stock markets is 4369. Even after taking into account the population of these countries, many advanced emerging markets have a lower number of listed stocks per one million people. For instance, Brazil has
1.8 stocks listed in the stock exchange per one million while the US has 13.1 stocks listed per one million people. Table 2.7 shows the detailed information regarding the size of advanced emerging markets versus the US. Therefore, advanced emerging markets are still relatively small in terms of both markets capitalization and number of stocks listed compared to developed markets.
Another important variable to consider especially in less developed markets is the liquidity. Bekaert, Harvey, and Lundblad (2007) suggest that the liquidity is an important risk factor affecting the expected stock returns in emerging markets. Table 2.7 shows the turnover ratio, which is the total value of traded stocks divided by the total market value as a proxy for liquidity. The table shows that the liquidity is generally lower in advanced emerging markets, with the ratio ranging between 25.8 and 185.2 for Mexico and Turkey respectively and an average of 62.5 in 2015 while the turnover ratio in the US is 165.1, more than double. The lower liquidity in advanced emerging markets presents extra risk for investors and can act as a barrier for arbitragers. This causes mispricing to be more persistent and anomalies to be more pronounced in these markets.
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2.4.Stock return and volatility patterns
Emerging stock market returns have different patterns compared to the US and other developed markets. For instance, emerging markets are generally considered as a riskier investment choice compared to more developed markets. Therefore, the expected returns are higher to compensate for the extra risk that investors have to bear (Bekaert & Harvey, 2014). Figure 2.5 shows the monthly average returns of the total market indexes in both local currency and USD denominated currency for the period January 1995 to December 2015. This period has been chosen because of data availability and comparison purposes. Note that this is related to the post-liberalization period in all of the advanced emerging markets. In local currency denominated returns, Taiwan has the lowest and Turkey the highest average monthly returns and six out of nine countries have higher average monthly returns compared to the US. However, the figure shows that the US dollar denominated returns have been lower relative to the US market in the sample period. This shows that the local currencies of eight out of nine advanced emerging markets depreciated against the US dollar causing the US denominated returns to be lower.
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Figure 2.5: Average Monthly Returns
3.00% 2.50% 2.00% 1.50% 1.00% 0.50% Local Currency
Monthly Return (%) 0.00% USD denominated
Country
As expected, the depreciation of the local currency has occurred mostly in
Turkey following the hyperinflation, which forced the country to revaluate the
Lira and subsequently removed six zeros from the currency in January 2005.
Furthermore, The Brazilian Real depreciated by almost 44% in two months after
Brazil started to use a floating system in January 1999. The figure also confirms that there was a huge depreciation of the Mexican Peso during the sample period, which is called the Mexican Peso crisis. Such depreciations add extra risk for foreign investors and create an expectation of higher returns in advanced emerging markets. It also creates a barrier for foreign arbitragers who arbitrage away any mispricing opportunity in these markets.
Many studies have examined the effects of liberalization on the expected stock returns. As discussed in previous sections, foreign investors would bid up the prices and therefore decrease the expected returns. Many papers report results consistent with this theory (Bekaert & Harvey, 2000; Henry, 2000). Figure 2.6 shows the average daily stock returns which denominated in the local currency of each country over the first and last half of the sample period (i.e. from January
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1995 to June 2005 and from July 2005 to December 2015). Although both of these periods belong to the post liberalization of advanced emerging markets, market integration is a gradual process and Figure 2.6 gives us an indication of any systemic change over time. It is clear from the figure that the average daily returns have decreased in six out of nine advanced emerging markets.
Figure 2.6: Average Daily Stock Returns 0.20% 0.18% 0.16% 0.14% 0.12% 0.10% 0.08% 0.06% First Half
Daily Return (%) 0.04% Second Half 0.02% 0.00%
Country
In addition to stock returns, the volatility characteristics of emerging markets are also different compared to more developed markets (Bekaert &
Harvey, 1997). In fact, the volatility of emerging market indexes is much higher compared to developed markets (Bekaert & Harvey, 2014). This higher volatility has caused higher downside risk in emerging markets. Bekaert and Harvey (2014) use data for the period 1992 to 2013 and document the 99% value at risk (VaR) for emerging markets which is as high as -24.6% compared to 14.7% for developed markets. In addition to higher volatility, studies argue that the return distribution is different in emerging markets and has slightly higher skewness and kurtosis compared to developed markets (Bekaert & Harvey, 2002). It should be noted that these volatility characteristics are similar pre- and post-liberalization;
69 | Page therefore, it can be argued that liberalization has not significantly affected the volatility characteristics of emerging stock returns (Bekaert & Harvey, 1997,
2002, 2003). The difference between the return distribution of emerging and developed markets is especially important when studying the MAX effect in advanced emerging markets. Higher skewness and kurtosis in emerging markets means that there are generally higher frequencies of more extreme positive returns. Therefore, the MAX anomaly could have different patterns in advanced emerging markets because the preference for positive skewness and lottery type assets is the main contributor of the MAX effect (Bali et al., 2011).
Table 2.8 shows the descriptive statistics of daily local currency denominated returns and volatility in emerging markets compared to the US for the entire sample period as well as for the first and second halves. The average daily returns and volatility of emerging stock markets are higher than the US in all periods, 0.0621% in the first half of the sample, which decreased dramatically to
0.0317% in the second half with the standard deviation of 0.0166 and 0.0129 respectively. The average standard deviation of daily returns in emerging markets ranges between 0.01181 and 0.02395 for South Africa and Turkey respectively, with the average of 0.01494 which is higher than 0.01187 for the US for the entire sample. In addition, the average skewness and kurtosis of daily stock returns in emerging markets are -0.09 and 13.14 respectively, higher than -0.27 and 11.14 for the US. Therefore, an advanced emerging stock market has higher average returns with higher volatility and higher skewness compared to the US stock market in the sample period. This is consistent with Bekaert and Harvey (2002)
70 | Page who suggest that, while investors can diversify away the extra risk in emerging markets, they can benefit from higher average returns.
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Table 2.8: Descriptive statistics of the daily returns and volatility characteristics of emerging stock markets versus US. All the returns shown in the table are the logarithmic returns (in percentage) denominated in the local currency of each country. The sample used in this study starts from January 1995 and finishes in December 2015. Therefore, the first half of the sample starts from January 1995 and finishes in June 2005 and the second half starts from July 2005 to December 2015.
Emerging Czech South Brazil Hungary Malaysia Mexico Poland Taiwan Turkey Markets' US Republic Africa Average Mean Return Full Sample 0.0490 0.0348 0.0496 0.0220 0.0570 0.0321 0.0536 0.0149 0.1101 0.0470 0.0357 First Half 0.0613 0.0498 0.0891 0.0088 0.0615 0.0482 0.0529 0.0088 0.1782 0.0621 0.0431 Second Half 0.0343 0.0209 0.0076 0.0368 0.0527 0.0169 0.0529 0.0206 0.0429 0.0317 0.0283 Standard Deviation Full Sample 0.0157 0.0128 0.0162 0.0124 0.0120 0.0149 0.0118 0.0147 0.0239 0.0149 0.0119 First Half 0.0165 0.0116 0.0168 0.0161 0.0133 0.0168 0.0114 0.0169 0.0299 0.0166 0.0111 Second Half 0.0147 0.0139 0.0158 0.0072 0.0108 0.0130 0.0122 0.0123 0.0164 0.0129 0.0126 Skewness Full Sample 0.1474 -0.3631 -0.5128 0.6179 0.0704 -0.1098 -0.6296 -0.0740 0.0134 -0.0934 -0.2720 First Half 0.3431 -0.2398 -0.8956 0.7087 0.0941 -0.0018 -1.0664 0.0046 0.0090 -0.1160 -0.1278 Second Half -0.1584 -0.4203 -0.0561 -1.2915 0.0137 -0.3608 -0.3033 -0.2569 -0.2621 -0.3440 -0.3663 Kurtosis Full Sample 10.3888 13.2892 10.3274 57.7207 6.1980 3.6889 7.5190 3.0667 6.0795 13.1420 11.1437 First Half 13.4841 2.5013 12.4891 39.5450 5.5437 3.1737 13.5461 2.2642 3.9760 10.7248 3.5360 Second Half 5.6675 17.7705 7.4510 16.5906 6.2175 3.6473 3.2633 3.3481 3.9703 7.5473 10.7486
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Apart from the return and volatility characteristics, the correlation between returns in developed and emerging markets is relatively low, which makes emerging markets an attractive opportunity to diversify away any country-specific risk in developed markets (Bekaert & Harvey, 2014; Harvey, 1995). The low correlation between stock returns in developed and emerging markets is particularly important to consider when studying the seasonal patterns of returns in advanced emerging markets. If their patterns follow the US and other developed stock market patterns, it might be because of the correlation between advanced emerging and developed markets. For instance, Aggarwal and Rivoli
(1989) argue that the correlation between the US and Asian stock markets causes lower average stock returns on Tuesdays in the Asian stock markets.
Bekaert and Harvey (2002) stated that the correlation between emerging and developed markets is sufficiently low and international investors can benefit from diversification. However, many studies argue that emerging markets have become more integrated with global markets. For instance, in a more recent study,
Bekaert and Harvey (2014) demonstrate that the correlation between emerging and developed markets has increased over the past 20 years. Figure 2.7 shows the correlation between individual emerging markets’ returns and the US. It is clear from the figure that the correlation is lower over the first half of the sample period
(i.e. January 1995 to June 2005) and has risen dramatically in the gradual process of market integration in the second half of the sample period (i.e. July 2005 to
December 2015). The increase in correlation of returns between emerging markets and the US market over time decreases the diversification benefits of foreign investors.
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Figure 2.7: Correlation of daily market returns with the US market
0.8 0.7 0.6
0.5 0.4 First Half 0.3 Second Half
Correlation 0.2 0.1 0 -0.1
Country
It is clear from Figure 2.7 that on average, Brazil and Mexico have the highest, whilst Malaysia and Taiwan have the lowest, correlation with the US market. This means that US investors would have highest diversification benefits by investing in Malaysia and Taiwan. To examine the time-variation, two-year rolling correlations between individual advanced emerging and the US markets are calculated, Figure 2.8 shows the rolling correlation of daily market returns for each advanced emerging market with the US market. Overall, the figure shows an increasing trend in the correlation between market returns over time. However, this increasing trend is not linear; rather it fluctuates over time with changing economic circumstances. The correlation between stock returns in emerging and developed markets tends to increase even further in the case of crisis (Forbes &
Rigobon, 2001; Loretan & English, 2000). Consistent with this notion, Figure 2.8 shows that the two-year period around the global financial crisis is higher for all of the advanced emerging countries.
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Figure 2.8: Two-year correlation of daily market returns with the US market
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Figure 2.9 shows the conditional correlation of returns between advanced emerging and the US markets for the period January 1995 to December 2015. The figure demonstrates the correlation when the US market return is negative and positive. Consistent with expectations that the correlation is higher during crisis, the correlation is higher for periods when the US market has negative returns, for all advanced emerging markets. This rise of correlation reduces the diversification benefit for foreign investors and reduces the attractiveness of investing in emerging markets. This means that, while financial markets in more developed markets are in a downturn and investors need diversification the most, the correlation between developed markets and emerging markets heightens.
Consequently, the diversification benefits will diminish.
Figure 2.9: Conditional correlation with the US stock market 0.6
0.5
0.4
0.3
0.2 Positive US Market Correlation 0.1 Negative US Market 0
-0.1
Country
2.5.Geographical location
As discussed in previous sections, emerging markets have a higher level of limits to arbitrage. In the absence of arbitragers, the price of stocks has higher deviations
76 | Page from the associated intrinsic value. This phenomenon is even more important in the case of emerging markets where there is a higher proportion of individual and less educated investors. These noise traders make decisions based on market sentiments and misperceptions regarding the financial market of the country. In such an environment, noise traders cause even more mispricing in the stock markets and thus, the noise trader risk is higher.
Various factors can influence the stock return behaviour through noise trading For instance, Hirshleifer and Shumway (2003) claim that weather can affect stock return patterns and Kamstra et al. (2003) propose the seasonal affective disorder hypothesis and argue that fewer numbers of hours of daylight can cause depression and hence, change the degree of risk aversion of investors and noise traders. Such variation in the behaviour of market participants can cause unique seasonal patterns in stock returns and volatility based on weather and the seasons of the country. Geographical location of the countries under study is the most important variable affecting the weather and seasons in the market.
Therefore, the geographical location of these markets can be an important issue to consider especially when examining seasonal patterns in stock returns and volatility. More specifically, it is necessary to know whether the country is in the northern or in the southern hemisphere. In addition, it is also important to study the latitude (i.e. the distance from the equator) for each advanced emerging country.
On the other hand, the longitude of the countries can also affect the stock return patterns. The reason for this is that it creates a time zone differential with the US market. Although the correlation of returns with the US stock markets is
77 | Page lower compared to developed markets, the time differential causes emerging markets to follow the US stock markets moderately (Solnik, Boucrelle, & Le Fur,
1996). The correlation and the time zone differential create new seasonal patterns in advanced emerging markets. For instance, many studies find evidence of lower than average returns on Tuesdays compared to other days of the week in
Australian and East Asian stock markets (Condoyanni et al., 1987; Jaffe &
Westerfield, 1985). Aggarwal and Rivoli (1989) argue that lower average Tuesday returns reflect the lower average Monday returns in the US stock market due to the time zone differentials. Therefore, the longitude of an advanced emerging market may be relevant to understanding the seasonality in returns.
The advanced emerging markets are spread geographically all around the world. Table 2.9 shows the exact geographical location of the capitals of the advanced emerging countries. Note that a smaller degree of latitude with the equator means that the country is closer to the equator. The impact of a country’s proximity to the equator is important because, a country like Malaysia with capital city latitude of 3ºN has year round tropical weather. Therefore, the seasonal affective disorder would not change the risk aversion and the misperception of noise traders in the Malaysian stock market. On the other hand, the seasonal effects should be more pronounced in countries like Poland, the Czech Republic and Hungary with capital city latitudes of 52ºN, 50ºN and 47ºN respectively. This means that the seasonal affective disorder argument of Kamstra et al. (2003) is most relevant in these countries with more pronounced changes of weather. In addition, the seasonal affective disorder should affect Brazilian and South African stock markets at the opposite time of the year because these countries are in the
78 | Page southern hemisphere. On the other hand, Malaysia and Taiwan are located in the
Far East with capital city longitudes of 101ºE and 121ºE respectively and are best suited to test the proposition of Aggarwal and Rivoli (1989) regarding the lower
Tuesday returns in Asian stock markets.
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Table 2.9: The geographical location of advanced emerging markets The latitude shows the degree differential to the equator. N stands for north and S stands for south. The longitude shows the degree differential to the prime meridian. E stands for east and W stands for west.
Country Capital city Latitude Longitude Brazil Brasilia 15°47'S 47°55'W Czech Prague 50°05'N 14°22'E Republic Hungary Budapest 47°29'N 19°05'E Malaysia Kuala Lumpur 03°09'N 101°41'E Mexico Mexico 19°20'N 99°10'W Poland Warsaw 52°13'N 21°00'E South Africa Johannesburg 26°20'S 28°05'E Taiwan Taipei 25° 2' N 121° 30' E Turkey Ankara 39°57'N 32°54'E
2.6.Conclusion
This chapter studied the characteristics of nine advanced emerging stock markets based on the categorization of financial time stock exchange. This study shows that these markets started to liberalize their market regulation in the late 1980s and early 1990s and started to attract more foreign investors by having fewer foreign ownership restrictions, developing their foreign exchange market and enforcing better corporate governance environments. Overall, advanced emerging markets increased their market capitalization, improved liquidity and introduced a floating exchange rate regime. However, despite these developments, these markets still have relatively lower market capitalization and liquidity, weaker corporate governance environments and higher fluctuations of exchange rates compared to developed markets.
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This study shows that the characteristics of advanced emerging markets are still different from those of developed markets, which imposes additional risk for investors. Such extra risk can act as an indirect barrier for foreign investment and put limits on arbitragers (Bekaert & Harvey, 2014). Therefore, having more limits to arbitrage can causes stocks in advanced emerging markets to have greater deviation from their intrinsic value and have greater mispricing. Therefore, anomalies are expected to be more pronounced in advanced emerging markets compared to developed markets.
More importantly, advanced emerging markets have different characteristics compared to developed markets that can directly affect the pervasiveness and patterns of seasonality and the MAX effect in particular. For instance, the dominant religion and culture of a country can introduce new seasonality in stock returns in these markets (Białkowski et al., 2012). Similarly, characteristics and the number of national holidays can directly affect the holiday effect. Different fiscal years in these countries can affect the prevalence of the
January effect and can introduce new seasonal patterns in stock returns
(Reinganum & Shapiro, 1987). The specific latitude and longitude of a country causes it to have different time zones, different number of hours of daylight during the day and different weather, which can cause variations in seasonality in stock returns and volatility in advanced emerging countries (Aggarwal & Rivoli,
1989; Hirshleifer & Shumway, 2003; Kamstra et al., 2003). In addition, the return distributions in advanced emerging markets are different compared to developed markets. More specifically, returns in these markets have higher skewness and kurtosis compared to developed markets, which means higher frequency of
81 | Page extreme positive returns. Therefore, the MAX effect might have different patterns and explanatory power in advanced emerging markets because according to Bali et al. (2011), the preference for extreme positive returns is the main reason behind the MAX anomaly. Lastly, restrictions on stock lending and short selling in the regulatory environment can work as a limit to arbitrage in advanced emerging markets. This is more prevalent for overvalued stocks because exploiting such mispricing opportunity requires investors to take short positions. Therefore, considering that most of the profit from the MAX strategy comes from the highest
MAX portfolio and most overvalued stocks (Bali et al., 2011), arbitragers cannot benefit from the MAX strategy and cannot correct the mispricing. Consequently, the MAX effect would be more persistent in advanced emerging markets.
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Chapter 3 : Seasonal anomalies in advanced emerging
stock markets
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3.1.Introduction
There is a considerable empirical research identifying different kind of seasonal patterns in stock returns in the US and other developed markets. These anomalies appear to be at odds with the efficient markets hypothesis in which stock prices reflect all available information and past returns cannot predict future returns
(Fama, 1970). Despite an extensive number of studies documenting evidence of seasonal anomalies in developed markets, only a few studies have comprehensively examined these anomalies within emerging markets.
Emerging markets comprise a growing share of global equity markets.
Hence, analysis of these markets is of increasing interest to international investors. It is widely argued that many emerging stock markets are not fully integrated with world markets, and hence constitute a separate asset class (Bekaert
& Harvey, 1995, 2000; Bekaert, Harvey, Lundblad, & Siegel, 2011). There are several reasons for this segmentation, but the main explanation is that regulations make it difficult for foreign investors to invest in equity across emerging markets.
Lack of market-based instruments for hedging emerging markets currency risk also reduces the attractiveness of these markets to international investors. This segregation is a limit to arbitrage that may result in emerging markets being less efficient than developed markets.
Bekaert and Harvey (2002) summarise the evidence for greater inefficiency in emerging markets by identifying three features that are consistent with this assertion. First, emerging market returns have higher serial correlations
(Harvey, 1995). Second, there is evidence of greater information leakage prior to
85 | Page public announcements in emerging markets (Bhattacharya, Daouk, Jorgenson, &
Kehr, 2000). Third, cross sectional trading strategies generate significant returns in emerging markets (Rouwenhorst, 1999; Van der Hart, Slagter, & Van Dijk,
2003). However, a more recent study by Griffin et al. (2010) shows that emerging markets are at least as efficient as developed markets; a result that is at odds with previous research.
In light of the conflicting evidence regarding the relative efficiency of developed and emerging markets, this chapter re-examines the issue of market efficiency in advanced emerging markets by testing for the significance of five seasonal anomalies: the month of the year effect (Rozeff & Kinney, 1976), the other January effect (Cooper et al., 2006), the day of the week effect (Cross,
1973), the holiday effect (Lakonishok & Smidt, 1988) and the ‘week 44 effect’
(Levy & Yagil, 2012). While these seasonal anomalies have been widely documented in developed markets, there is little evidence regarding them in advanced emerging markets; that is, Brazil, Czech Republic, Hungary, Malaysia,
Mexico, Poland, South Africa, Taiwan and Turkey. As of March 2014, these markets were classified as advanced emerging markets by the Financial Times
Stock Exchange (FTSE, 2014)1.
Advanced emerging markets have different characteristics compared to developed markets that can affect the seasonal patterns of returns and volatility.
For instance, these markets are generally characterised as having lower levels of liquidity, market capitalisation and openness, higher volatility and a smaller
1 The FTSE classifies stock markets into developed, advanced emerging, secondary emerging and frontier based on a set of criteria: quality of market, market size, stability, and market access, among others (FTSE, 2014)
86 | Page proportion of institutional investment. In addition, these markets are spread geographically around the world; hence, they have different time zones, calendars and holidays. These different characteristics create a unique laboratory to: firstly, perform out-of-sample tests of seasonal anomalies and secondly, examine the applicability of theoretical and institutional explanations that have been proposed for these predictable patterns in returns. This study focuses on advanced emerging markets rather than secondary emerging or frontier markets, as the latter markets are typically characterised as having data availability issues and a low level of liquidity, which may cause errors in the estimation of returns (Blume &
Stambaugh, 1983).
The remainder of this study proceeds as follows: Section 2 provides a review of the literature; the data used in the study are explained in Section 3; methodology and results are documented in Section 4; Section 5 provides robustness check; and, Section 6 provides a summary.
3.2.Literature review
3.2.1. The month of the year effect
Stock returns have been shown to be significantly higher in January relative to other months of the year (Rozeff & Kinney, 1976). This ‘January effect’ has been shown to be pervasive across a range of developed markets and across different sample periods (Keim, 1983; Mehdian & Perry, 2002; Reinganum, 1983).
However, more recent studies indicate that the January effect has disappeared
87 | Page from most international equity markets worldwide (Darrat, Li, Liu, & Su, 2011), while Zhang and Jacobsen (2012) use 300 years of data to show that monthly patterns in returns are sample-specific. Given these mixed results regarding the significance of the January effect, there is considerable merit in examining its prevalence in emerging markets as an out-of-sample test of the robustness of this seasonal anomaly.
The tax-loss selling hypothesis has been widely proposed as an explanation for the January effect. Branch (1977) argues that investors tend to sell their losing stocks at the end of the tax year (December) to realise a capital loss and subsequently buy back these stocks following the start of the next tax year
(January). From this perspective, studying the January effect in advanced emerging markets would be a good out-of-sample test, because eight out of nine countries in the sample have the same fiscal year as the US, while South Africa has an April to March tax year. Therefore, if the tax-loss selling hypothesis plays an important role in explaining monthly patterns of returns, the month of January should exhibit abnormal returns for all countries in the sample except South
Africa.
An alternative explanation for the January effect is the window dressing hypothesis (Lakonishok & Smidt, 1988). According to this explanation, fund managers want to attract more investors by showing their winning stocks and downplaying their losing stocks. Therefore, they tend to sell their loser stocks at the end of December and start buying them back at the beginning of January.
However this hypothesis, would be expected to have a relatively weak effect on returns in the emerging markets which are used in this study, because emerging
88 | Page markets comprise fewer institutional investors compared with developed markets
(Voronkova & Bohl, 2005).
3.2.2. The other January effect (January barometer)
It has been shown that market returns in January can be used as a signal for the performance of the market for the rest of the year (Cooper et al., 2006). For example, Hensel and Ziemba (1995) show that, in 86% of instances, when the
January return is positive the subsequent eleven month returns are also positive.
Evidence of the other January effect has also been reported for different sample periods (Brown & Luo, 2006) and across international markets including Canada,
Australia and the UK (Hensel & Ziemba, 1995). However, recent studies have cast doubt on the efficacy of the other January effect (Easton & Pinder, 2007;
Marshall & Visaltanachoti, 2010). Despite this conflicting evidence in developed markets, no studies have comprehensively examined this effect across a broad sample of less developed markets.
The main explanation for the other January effect is the institutional investment committee theory (Little & Albrecht, 2006). This theory asserts that the first investment result of the year appears at the end of January and powerful institutions cannot afford to disregard good investment opportunities. Therefore, they will invest their funds based on the market’s performance in January.
However, this theory is inconsistent with recent studies that suggest the other
January effect has either diminished or disappeared despite an increase in the proportion of equity owned by institutional investors in the US market over time
89 | Page
(Stivers, Sun, & Sun, 2009). Emerging markets have fewer institutional investors compared to their developed counterparts. Therefore, the other January effect is expected to be less pronounced in emerging markets.
3.2.3. The day of the week effect
The day of the week effect implies that stocks generate different average returns on different days of the week (Apolinario, Santana, Sales, & Caro, 2006). As markets are closed during the weekend, returns should be expected to be higher on
Mondays, as it is effectively a three-day return (French, 1980). In contrast to this expectation, empirical studies suggest that Monday’s average returns are lower than the average daily returns for the rest of the days of the week in the US markets (Cross, 1973), while average Friday returns are significantly higher than the rest of the week (Gibbons & Hess, 1981; Lakonishok & Levi, 1982). These results were confirmed by Kiymaz and Berument (2003), who used a Generalized
Autoregressive Conditional Heteroscedastic (GARCH) model and reported evidence for the day of the week effect in both daily returns and volatility in the
US for 1973 to 1997.
Given the robust evidence of the day of the week effect in the US markets, attention has turned to international markets to examine whether this anomaly is a global phenomenon (Lakonishok & Smidt, 1988) or whether it is due to specific institutional behaviour in the US stock market (Jaffe & Westerfield, 1985). Most international studies have found evidence to support the existence of the negative
Monday and positive Friday effect as an international phenomenon. However, the
90 | Page empirical findings show that the day of the week effect does not exhibit the same patterns in some countries. For example, Brooks and Persand's (2001) study of
Asian stock markets found evidence of negative average returns on Wednesday for Taiwan. They also reported negative average returns on Tuesday and higher average returns on Monday for Thailand and Malaysia. Negative Tuesday returns have also been found in the Japanese market (Jaffe & Westerfield, 1985) and for
Australia, Japan and Singapore (Condoyanni et al., 1987).
Many studies argue that the negative Tuesday effect in international markets can be attributed to differences in time zones compared to the US stock market (Aggarwal & Rivoli, 1989). Although international markets have high correlation with the US stock market, they cannot react to the movement of the
US stock market immediately as it has up to a 13-hour difference. Therefore, international markets located in different time zones will react to negative
Monday returns in the US stock market on Tuesdays. The sample consists of advanced emerging markets, which are spread geographically around the world with different time zones. For example, there is a 13-hour difference between
Malaysia and Taiwan and the US markets. These time differences allow for a comparison across countries to examine the effect of time zones on the day of the week effect.
An alternative hypothesis for the day of the week effect is that volatility is higher on Friday and lower on Monday, resulting in an increased required rate of return on Friday and a decreased one on Monday (Campbell & Hentschel, 1992). This hypothesis has been supported by empirical studies that show both volatility and returns are higher on Fridays (French & Roll, 1986). This study investigates the
91 | Page seasonal patterns in both returns and volatility of returns across advanced emerging markets and thus can examine if the day of the week effect has been caused by different average volatility and risk on different days of the week.
3.2.4. Holiday effect
The holiday effect refers to the abnormal positive return of stocks on days preceding public holidays (Fields, 1934). The holiday effect has been shown to be robust across different markets and sample periods (Liano, Marchand, & Huang,
1992; Wilson & Jones, 1993). Lakonishok and Smidt (1988) examined the Dow
Jones Industrial Average data from 1897 to 1986 and found statistically and economically higher returns on trading days which precede public holidays (the pre-holiday effect). Their empirical results also reveal a significant post-holiday effect after 1952 (Lakonishok & Smidt, 1988). Studies have also reported evidence of a pre-holiday effect in the UK and Japan (Kim & Park, 1994),
Australia (Easton, 1990; Marrett & Worthington, 2009) and New Zealand (Cao,
Premachandra, Bhabra, & Tang, 2009). Chan et al. (1996) conducted a comprehensive study and found evidence of the pre-holiday effect before Asian cultural holidays and reported significant abnormal returns before Hindu holidays in India, Islamic New Year and Vesak in Malaysia and Chinese New Year in
Singapore. Similarly, Bley and Saad (2010) report significant positive returns on the days preceding middle-eastern religious holidays. These findings show that the holiday effect exists in many developed and emerging markets and is independent of the type of holiday. However, no study to date has
92 | Page comprehensively studied the holiday effect across the broad sample of advanced emerging markets.
Many studies tend to explain the holiday effect using behavioural explanations. Kavanagh and Bower (1985) study the effect of happiness and sadness on investors’ behaviour. They report that happier investors tend to believe in better and more positive outcomes (Kavanagh & Bower, 1985). Similarly,
Fabozzi, Ma, and Briley (1994) suggested that the good mood around the holidays may cause investors to believe in more favourable outcomes. Another possible behavioural explanation for the holiday effect is inventory adjustment hypothesis.
Ariel (1990) hypothesised that short sellers tend to close their risky positions in advance of holidays. This hypothesis was supported by his finding that investors prefer to buy and avoid selling their stocks before holidays (Ariel, 1990). This argument is not valid in the case of many emerging markets because of short selling constraints (Li, Sarkar, & Wang, 2003).
3.2.5. The week of the year effect
Levy and Yagil (2012) identified a week of the year effect, whereby they demonstrated abnormal returns occurring in the 44th week of the year. They studied the Standard and Poor’s (S&P) 500 for the period 1950 to 2010 and found that this 44th week, which starts on October 29th and ends on November 4th, has had significantly higher returns in comparison to other weeks of the year. They used 1950 to 2008 for their estimation period and the next two years for their prediction period and found that the trading strategy derived from these findings
93 | Page beat the simple buy and hold strategy (Levy & Yagil, 2012). To show that their results are not sample-specific, Levy and Yagil (2012) also examined 19 developed markets and found evidence of the week 44 effect in 18 out of 19 international countries, with 17 being statistically significant (Levy & Yagil,
2012). Despite this robust evidence of positive abnormal returns on the 44th week of the year in developed markets, this seasonal pattern has not been examined in emerging markets.
It has been proposed that the week 44 effect may be explained by behavioural factors. Seasonal Affective Disorder (SAD) has been suggested by
Kamstra et al. (2003), who claim that shorter days during the winter can cause depression as investors have relatively fewer hours of daylight. They suggest that this depression can change investors’ behaviour. It is worth noting that, in the southern hemisphere, daylight saving mostly commences during the 44th week of the year, which starts on 29th October and ends on 4th November. Six out of nine advanced emerging markets have daylight saving, yet Malaysia, South Africa and
Taiwan do not. However, the exact effects of SAD on investors are still unclear.
Many studies show that investors are more risk averse during fall and they are willing to take riskier positions during winter (Dolvin, Pyles, & Wu, 2010). On the other hand, Kramer and Weber (2012) show that the SAD effect can affect investors’ risk aversion and indicate that investors would prefer safer choices during the winter. The sample used in this study consists of South Africa, which is located in the southern hemisphere; Brazil and Malaysia, which are equatorial and six countries in the northern hemisphere. Therefore, this study examines the out- of-sample robustness of the week 44 effect and uses this panel of emerging
94 | Page countries with differing characteristics to examine whether SAD is a valid explanation for this anomaly.
3.3.Data
This study focuses on nine advanced emerging markets based on the FTSE’s country classification, which are Brazil, Czech Republic, Hungary, Malaysia,
Mexico, Poland, South Africa, Taiwan and Turkey. The study examines the seasonal patterns of returns and volatility of the Datastream market indices for each country. These indices have been used because they have a longer time series of data and represent a greater proportion of market capitalisation of each market compared to market indices developed by other providers.
It is important to consider any possible financial and/or economic event in these markets in the sample period because extreme events may affect the results.
For instance, Turkey revaluated the Lira and removed six zeros from its currency in January 2005 following hyperinflation in the 1990s and the early 2000s.
Furthermore, Brazil and Malaysia moved from a pegged to floating exchange rate regime. The Brazilian Real was converted from a floating to a pegged system in
January 1999 and depreciated by almost 44% in two months. There was also a huge depreciation of the Mexican Peso in the beginning of 1995, which is called the Mexican Peso crisis.
To test whether the results have been affected by extreme economic and financial events in each country, both the US dollar and local currency denominated returns have been used. However, this research finds similar results
95 | Page and equivalent seasonal patterns in stock returns for data denominated in the US dollar and the local currency of each country. Therefore, as expected, although extreme financial and economic events during the sample period have affected returns, no seasonality has been introduced by such events.
Table 3.1 reports information about the sample period used in this study and market characteristics for the nine advanced emerging countries. The sample includes at least 20 years of data for each market index, ranging from 21 years for
Brazil to 41 years for South Africa. The choice of the sample period for each market is guided by data availability. Despite FTSE (2014) classifying all countries in the sample as being advanced emerging, significant differences in the characteristics across these markets are evident, providing a diverse environment within which to test the efficacy of seasonal anomalies outside developed markets.
According to Charoenrook and Daouk (2005), short selling is not permitted in
Malaysia. Despite the fact that short selling is legal in other advanced emerging countries, it is not feasible in Brazil, Hungary and Poland and limited to only liquid stocks in the Czech Republic, Mexico, South Africa, Taiwan and Turkey. In comparison to advanced emerging markets, short selling can be easily practiced with a wider range of stocks in the US market. The start and end dates of the fiscal year in advanced emerging markets are similar to that of the US, which is January to December, except in South Africa which has a fiscal year that begins in April and ends in March. The annual local currency denominated returns ranges between 5.14% for Taiwan and 49.46% for Turkey and the annual US dollar denominated returns range between 2.08% for Hungary and 13.99% for Mexico.
The difference between the local and the US denominated returns for each country
96 | Page shows the depreciation of the currency of the country relative to the US dollar, which can be important, particularly for foreign investors. The turnover ratio of traded stocks ranges between 25.31 for Mexico and 136.51 for Turkey. The table also reports the disclosure requirement index, liability standard index and the public enforcement index as defined and calculated by Porta et al. (2006). These indices take values between zero and one and show that these countries have relatively weaker shareholders’ protection compared to the US. The anti-self- dealing index measures the strength of minority shareholder protection against self-dealing by the controlling shareholders (Djankov et al., 2008) and ranges between 0.17 for Mexico and 0.95 for Malaysia. Note that, based on the results of
Djankov et al. (2008), the US market has relatively stronger minority shareholder protection compared with advanced emerging markets, with the exceptions being
Malaysia and South Africa.
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Table 3.1: General market information on advanced emerging markets
Country Brazil Czech Republic Hungary Malaysia Mexico Poland South Africa Taiwan Turkey US Sample Start Date 4/07/1994 9/11/1993 21/06/1991 2/01/1986 10/05/1989 1/03/1994 1/01/1973 2/05/1988 4/01/1988 1/01/1974 Sample Finish Date 2/04/2014 2/04/2014 2/04/2014 2/04/2014 2/04/2014 2/04/2014 2/04/2014 2/04/2014 2/04/2014 2/04/2014 Short-selling Legality a Yes Yes Yes No Yes Yes Yes Yes Yes Yes Short-selling Feasibility a No Yes No No Yes No Yes Yes Yes Yes Fiscal Year Jan-Dec Jan-Dec Jan-Dec Jan-Dec Jan-Dec Jan-Dec Apr-Mar Jan-Dec Jan-Dec Jan-Dec GDP per capita (USD) b 11067.47 18985.15 13153.84 11062.04 10836.68 14329.78 6354.27 21571.62 10518.28 54678.17 Annual Local Currency 14.45% 8.36% 12.31% 11.44% 22.03% 9.92% 19.14% 5.14% 49.46% 11.3% Denominated Returns
Annual US Dollar 8.08% 11.77% 2.08% 10.25% 13.99% 7.86% 11.73% 4.63% 8.95% 11.3% Denominated Returns GDP (current billion US$) 2245.67 208.80 133.42 313.16 1260.91 525.87 350.63 - 822.14 16770 (2013)c GDP growth (annual %) 2.49 -0.70 1.53 4.73 1.07 1.67 1.89 - 4.12 2 (2013)c Stocks traded, turnover 67.88 27.04 54.59 28.57 25.31 42.56 54.93 - 136.51 125 ratio (%) (2012)c Market capitalization of listed companies (% of 54.69 17.97 16.62 156.04 44.25 35.82 160.15 - 39.14 115 GDP) (2012)c Market capitalization of listed companies (current 1229.85 37.16 21.08 476.34 525.06 177.73 612.31 - 308.77 18668.33 billion US$) (2012)c Disclosure requirements 0.25 - - 0.92 0.58 - 0.83 0.75 0.5 1 index d Liability standard index d 0.33 - - 0.66 0.11 - 0.66 0.66 0.22 1 Public enforcement index d 0.58 - - 0.77 0.35 - 0.25 0.52 0.63 0.90 Anti-self-dealing index e 0.27 0.33 0.18 0.95 0.17 0.29 0.81 0.56 0.43 0.65
98 | Page
Sources: a. Charoenrook and Daouk (2005) b. International Monetary Fund (2014) c. The World Bank (2015) d. Porta et al. (2006) e. Djankov et al. (2008)
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Table 2.4 in Chapter 2 presents the list of holidays in advanced emerging stock markets. It shows that the number of stock exchange holidays varies from country to country, ranging from ten days during the year in Mexico, to twenty days in Malaysia. Religious and cultural events are more prevalent in multi-ethnic countries like Malaysia. These holidays have been crosschecked against data obtained from Datastream to ensure that there has been zero trading volume on these reported holidays.
The return indices examined in this study incorporate both dividend payments as well as capital gains. The logarithmic returns have been calculated for the Datastream market return indices. Table 3.2 shows descriptive statistics of total returns in the local currency.
Panel A of Table 3.2 shows the descriptive statistics of daily returns denominated in local currency of each country. The average daily returns range between 0.0178% for Poland and 0.1477% for Turkey. The skewness ranges between -0.637 for South Africa and 0.720 for the Czech Republic. Panel B of
Table 3.2 shows the descriptive statistics of weekly returns. The average weekly returns range between 0.0902% for Poland and 0.7302% for Turkey. The skewness ranges between -0.734 for South Africa and 0.478 for the Czech
Republic. Panel C of Table 3.2 shows the descriptive statistics of the monthly returns for the entire sample. The number of months examined ranges between
236 for Brazil and 495 for South Africa. The average monthly returns range between 0.3871% for Poland and 3.1513% for Turkey. The skewness ranges between -0.852 for South Africa and 0.980 for Hungary. Turkey has greater mean daily, weekly and monthly returns than other countries primarily because of its
100 | Page experience of hyperinflation especially during the 1990s. While advanced emerging markets has been classified in same group by FTSE (2014), the descriptive statistics reported in Table 3.2 demonstrates that there are significant differences in the characteristics of returns across these markets.
101 | Page
Table 3.2: Descriptive statistics of market index returns in the local currency The table shows the descriptive statistics of daily/weekly/monthly returns in advanced emerging markets. Note that the sample period used in this study is different for each of the countries because of data availability issue. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively. Descriptive statistics of returns in percentage
ions
- value Period of Number observat Mean P St. Dev. Minimum Maximum Skewness
Panel A: Percentage daily logarithmic returns Brazil 1994-2014 5151 0.0590 0.008** 1.6033 -10.5538 19.5492 0.105 Czech 1993-2014 5320 0.0357 0.061 1.3882 -14.1854 21.7610 0.720 Republic Hungary 1991-2014 5942 0.0456 0.030* 1.6170 -17.9365 13.2481 -0.455 Malaysia 1986-2014 7368 0.0446 0.004** 1.3294 -22.1980 20.4016 -0.228 Mexico 1989-2014 6494 0.0832 0.000** 1.3053 -9.9208 10.6053 0.067 Poland 1994-2014 5240 0.0178 0.453 1.7103 -10.4405 16.2640 -0.230 South 1973-2014 10761 0.0674 0.000** 1.2768 -14.5156 13.6043 -0.637 Africa Taiwan 1988-2014 6761 0.0194 0.373 1.7960 -10.2945 12.7382 -0.011 Turkey 1988-2014 6846 0.1477 0.000** 2.5097 -19.4534 17.0313 -0.003 Panel B: Percentage weekly logarithmic returns Brazil 1994-2014 1041 0.2820 0.012* 3.6025 -21.0090 18.3440 -0.627 Czech 1993-2014 1075 0.1674 0.101 3.3469 -24.2584 33.1181 0.478 Republic Hungary 1991-2014 1201 0.2281 0.039* 3.8354 -28.4039 22.5905 -0.442 Malaysia 1986-2014 1489 0.2170 0.008** 3.1350 -26.0195 21.2592 -0.653 Mexico 1989-2014 1313 0.4100 0.000** 3.1426 -15.2550 13.9600 -0.118 Poland 1994-2014 1059 0.0902 0.483 4.1954 -25.1573 28.0574 -0.348 South 1973-2014 2176 0.3334 0.000** 2.9814 -21.0747 14.3617 -0.734 Africa Taiwan 1988-2014 1366 0.0926 0.424 4.2846 -23.5507 21.2471 -0.091 Turkey 1988-2014 1383 0.7302 0.000** 6.1396 -30.8163 31.6625 0.021 Panel C: Percentage monthly logarithmic returns Brazil 1994-2014 236 1.2159 0.014* 7.5489 -31.2797 23.9021 -0.534 Czech 1993-2014 244 0.7387 0.129 7.5544 -24.6535 53.4558 0.939 Republic Hungary 1991-2014 273 1.0009 0.072 9.1782 -39.2512 61.4055 0.980 Malaysia 1986-2014 338 0.9844 0.019* 7.6835 -41.4812 33.5029 -0.775 Mexico 1989-2014 298 1.7520 0.000** 6.8526 -26.6241 21.3749 -0.327 Poland 1994-2014 241 0.3871 0.529 9.3934 -38.9493 33.3370 -0.490 South 1973-2014 495 1.4657 0.000** 6.7109 -39.1791 25.0116 -0.852 Africa Taiwan 1988-2014 311 0.4218 0.453 9.9014 -45.8517 41.5874 -0.011 Turkey 1988-2014 314 3.1513 0.000** 13.8200 -52.5619 54.9837 0.361
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3.4.Methodology and results
3.4.1. The month of the year effect
To examine the month of the year effect across nine advanced emerging markets, monthly data has been used to calculate the monthly logarithmic returns for the given indices. To compare returns in each month of the year, a regression model is estimated with 12 dummy variables for calendar months, excluding the intercept term (Ariss, Rezvanian, & Mehdian, 2011). The regression equation is specified as follows:
= , + (3.1) 12 𝑟𝑟𝑡𝑡 ∑𝑖𝑖=1 𝛼𝛼𝑖𝑖𝐷𝐷𝑖𝑖 𝑡𝑡 𝜀𝜀𝑡𝑡 where is the logarithmic return of a given market index over the period
𝑡𝑡 t-1 to t; = {𝑟𝑟1 for January, 2 for February, … … , 12 for December }, is the
𝑖𝑖 average return𝑖𝑖 of month i, and is the dummy variable which is equal𝛼𝛼 to 1 for
𝑖𝑖 month i and zero otherwise. Equation𝐷𝐷 (3.1) is estimated without an intercept term since it includes dummy variables for all 12 months of a calendar year. It has been shown that the volatility of returns in emerging markets varies across time
(Bekaert & Harvey, 2002). The ordinary least squares estimator has been used to estimate equation (3.1) where the ARCH LM test (Engle, 1982) cannot reject the null hypothesis of constant variance of error term across time. Where the
𝑡𝑡 ARCH LM test does provide evidence of time-varying𝜀𝜀 error variance, the
GARCH model of the following form is used: h = + + h (3.2) 2 t λ1 λ2ε𝑡𝑡−1 λ3 t−1
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In equation (3.2), h is the conditional error variance for month t, and
t , and are GARCH model parameters. Equations (3.1) and (3.2) are
1 2 3 λestimatedλ usingλ the maximum likelihood estimator. To examine the seasonal patterns of volatility in each market, the conditional volatility series (h ) predicted
t from equation (3.2) has been regressed on 12 dummy variables �for calendar months as shown in equation (3.3):
h = D , + u (3.3) 12 �t ∑i=1 βi i t t In equation (3.3), is the average variance of the given stock index in
i month i. The Wald test is βused to test the null hypotheses of = and =
i j i j for i j so that the monthly seasonality of returns and volatilities,α α respectively,β β can ∀be ≠examined.
Table 3.3 provides the results from the estimation of equation (3.1). The mean January return ( ) is positive for all countries, but it is statistically
𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽 different from zero at𝛼𝛼 the 5% level for only three countries (Czech Republic,
Hungary and Turkey). Overall, there is little evidence of the January effect across advanced emerging markets. This result is consistent with other recent studies, which argue that the January effect has disappeared in international markets
(Darrat et al., 2011). While there is no evidence of a significant January effect, returns in December do appear to be higher than other months, similar to the finding of Ariss et al. (2011) for Gulf Cooperation Council stock markets. The
December mean return ( ) is positive and statistically significant for all
𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 but Brazil, Poland and Taiwan.𝛼𝛼 The magnitude of the average return in December is higher than returns for both January and the remaining months with only two
104 | Page exceptions: Hungary and Turkey. In these two countries, January returns are slightly higher than December returns and both months have greater mean returns compared with other remaining months. Lastly, non-parametric tests also indicate that advanced emerging markets yield higher returns in the month of December.
The average proportion of positive excess returns (PP) across all advanced emerging markets is 70.3% for December, which is significantly higher compared to January (54.2%) and other ten remaining months (48.5%).
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Table 3.3: Results for the month of the year effect The table shows the regression results for equation (3.1) for January and December for all advanced emerging countries. PP is the proportion of positive monthly excess returns in the entire sample period and p-value (given in parenthesis) is the significance of binomial test. It indicates if the proportion is statistically different from 50% for all days. ** indicates significance at the 0.01 level and * indicates significance at the 0.05 level Returns denominated in local currency of each country Country Years January December All Remaining Months All Months
PP PP pp pp
20 1.8214 0.400 3.1593 0.700 0.9558 0.505 0.513 Brazil 𝛼𝛼𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽𝐽 𝛼𝛼𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 𝛼𝛼𝐹𝐹𝐹𝐹𝐹𝐹−𝑁𝑁𝑁𝑁𝑁𝑁 (0.289145) (0.1201) (0.0658) (0.0369*) (0.0563) (0.0481*)
21 3.9498 0.667 4.0282 0.860 0.0629 0.460 0.512 Czech Republic (0.01552*) (0.0554) (0.0131*) (0.0006**) (0.0298*) (0.0474*)
23 6.6326 0.609 4.0139 0.610 0.1250 0.458 0.484 Hungary (0.000432**) (0.0974) (0.0324*) (0.0974) (0.0239*) (0.0416*)
28 1.2718 0.607 4.5956 0.820 0.5974 0.507 0.541 Malaysia (0.37886) (0.0800) (0.0013**) (0.0003**) (0.0461*) (0.0136*)
25 0.8905 0.440 4.1176 0.800 1.6004 0.504 0.523 Mexico (0.515692) (0.1328) (0.0025**) (0.0015**) (0.0502) (0.0333*)
20 2.9849 0.500 2.7209 0.650 -0.1036 0.498 0.510 Poland (0.158539) (0.1762) (0.1970) (0.0739) (0.0561) (0.0488*)
41 1.5081 0.500 3.8424 0.660 1.2249 0.515 0.525 South Africa (0.147058) (0.1224) (0.0002**) (0.0160*) (0.0330*) (0.0191*)
26 2.8139 0.615 2.6857 0.650 -0.0456 0.494 0.518 Taiwan (0.147058) (0.0792) (0.1645) (0.0465*) (0.0487*) (0.0372*)
26 7.9317 0.538 7.8745 0.580 2.2082 0.424 0.446 Turkey (0.003076**) (0.1439) (0.0033**) (0.1151) (0.0023*) (0.0072**)
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The Wald test is used to test whether particular months of the year exhibit abnormal returns. In unreported results, the hypothesis that the mean returns in
January are equal to the mean returns for other months of the year is rejected for the Czech Republic, Hungary, Malaysia, Taiwan and Turkey. Hence, the January effect has been detected in five out of nine advanced emerging markets. For instance, performing a Wald test shows that January returns are statistically higher than September returns in Taiwan with a p-value of 0.031, while January returns are statistically higher than February, May and August in Turkey with p-value of
0.031, 0.006 and 0.006, respectively. Given the earlier evidence, which suggests returns in advanced emerging markets are higher in December, the Wald test is also used to test if December returns are statistically higher than other months of the year. The results indicate that this is the case in seven out of nine advanced emerging markets. For instance, December returns in the Czech Republic are statistically higher than June, September and November returns with the Wald test p-value of 0.008, 0.045 and 0.009, respectively, while December returns in
Malaysia are statistically higher than March, June, August, September and
November returns with a p-value of, at most, 0.035. For Brazil and Poland, the
December return is not statistically different from those for other months at the
5% significance level.
Finally, the ARCH LM test has been conducted to check whether error variance varies across time. The null hypothesis of the test could not be rejected in any of advanced emerging countries. South Africa has a p-value of 0.215 for the
ARCH LM test statistic, the lowest among all advanced emerging markets.
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Therefore, monthly seasonality in the volatility of returns is not examined as the monthly return series follows a homoscedastic process.
Although January returns tend to be higher, the results reported in Table
3.3 show the existence of a relatively stronger December effect compared to
January. This can be at odds with many studies in developed markets, which report strong evidence of the January effect (Mehdian & Perry, 2002; Mills &
Andrew Coutts, 1995). This is not surprising given that the January effect has been argued to be a sample-specific phenomenon (Zhang & Jacobsen, 2012).
Relatively weaker evidence of the January effect in advanced emerging markets appears to support this notion that the January effect cannot be supported out-of- sample. It is essential to consider the differences and similarities between advanced emerging and developed markets before drawing any conclusion. One of the most important issues related to the January effect is the fiscal year of these countries. In advanced emerging markets, eight out of nine countries have a
January to December tax year similar to the US where empirical results first found a strong January effect (Rozeff & Kinney, 1976). The fiscal year in South Africa is April to March and not the same as the US. Note that this study finds no
January effect in this market.
Emerging markets have different characteristics compared to developed markets. Voronkova and Bohl (2005) argue that institutional traders do not affect the stock prices in emerging markets, as in the US and more developed markets.
This difference may explain the differences in seasonal patterns identified in
Table 3.3. One explanation for the January effect in developed markets is tax-loss
108 | Page selling by institutional investors (Branch, 1977). However, in the case of emerging markets, the characteristics of investors are different. Individual investors own a higher proportion of capital and the influence of institutional investors is less pronounced in emerging markets (Voronkova & Bohl, 2005).
Transaction costs are also higher in emerging markets. These two factors could limit the effects of the tax-loss selling hypothesis, thereby reducing the January effect in eight advanced emerging markets with the January to December tax year.
Lakonishok and Smidt (1988) propose that the January effect may be explained by the window dressing hypothesis, whereby institutional investors sell their bad stocks in December and buy them back in January to report a better performance to investors. As with the tax-loss selling argument, fewer institutional investors in emerging markets would reduce the prevalence of window dressing (Voronkova & Bohl, 2005). Therefore, January returns in emerging markets might not be expected to be as influenced by window dressing as compared with developed markets.
More persistent than the January effect, the results of this study show that
December returns are about six times higher than returns for other months in advanced emerging markets (see Table 3.3). This research finds no theoretical explanation for higher December returns in these markets. On the other hand, it can further support the argument of Zhang and Jacobsen (2012) that the month of the year effect is sample-specific and may vary over time.
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3.4.2. The other January effect
To examine the other January effect, monthly logarithmic returns are used. The ordinary least squares regression method has been used as suggested by Cooper et al. (2006). To study the other January effect, a dummy variable (D) is created, which is equal to one for 11 months of the year following a positive return in
January and equal to zero for the 11 months following a negative return in
January. The regression equation is specified as follows:
r , = + D + (3.4)
Feb−Dec t µ δ t εt Where r , is the average monthly return for the 11-month period
Feb−Dec t (February to December) in year t; denotes the average monthly return for the
11-month period following a negative𝜇𝜇 January; is the difference between average returns for the month followed by a negative𝛿𝛿 and a positive January; and is the error term. Following Cooper et al. (2006), the other January effect isε examined using raw as well as excess index returns over the Treasury bill rate2.
The null hypothesis of = 0 against the alternative hypothesis > 0 is tested to investigate the other Januaryδ effect. δ
Table 3.4 shows the results using raw returns. As the table shows, only
Brazil has the positive and statistically significant return spread between the months following positive and negative Januarys and in four out of nine countries, the spread is even negative, ranging between -1.99% for Taiwan and 2.63% for
Turkey.
2 The results from the use of excess returns are not presented in this thesis as they are similar to those obtained from the use of raw returns. These results are available upon request.
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Table 3.4: Results for the other January effect (denominated in local currency of each country) The average monthly returns for 11-months holding periods following negative and positive Januarys denominated in local currency of each country. All returns have been denoted monthly and are in percentage terms. N shows the number of months in the calculation. * indicates significance at the 0.05 level.
Positive Januarys Negative Januarys
Country Years Return (%) N Return (%) N Spread (%) t-stats
Brazil 20 2.0976 110 0.0004 101 2.0972 2.054*
Czech Republic 21 0.6161 165 -0.3165 57 0.9326 0.905
Hungary 23 0.4149 165 0.9153 79 -0.5004 -0.456
Malaysia 28 1.2068 187 0.3972 112 0.8095 0.873
Mexico 25 1.3916 143 2.1732 123 -0.7816 -0.933
Poland 20 0.6542 110 0.5933 101 0.0609 0.055
South Africa 41 1.0426 264 2.0473 189 -1.0047 -1.579
Taiwan 26 -0.5576 176 1.4317 101 -1.9892 -1.691
Turkey 26 3.6013 209 0.9668 68 2.6345 1.374
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There are two possible explanations for these outcomes. The results suggest that the other January effect does not exist in advanced emerging markets supporting the study of Bohl and Salm (2010) who argue that it is not an international phenomenon and a result of data-snooping. On the other hand, as mentioned in the literature review section, an alternative explanation for the other
January effect is the institutional investment committee theory suggested by Little and Albrecht (2006). They claim that institutional investment committees analyse the performance of their portfolio at the end of January to make investment decisions for the rest of the year. This phenomenon causes the market to react and consequently, the other January effect appears (Little & Albrecht, 2006).
However, institutional investors in emerging markets tend to hold relatively less capital and do not affect the stock market as in developed markets (Voronkova &
Bohl, 2005). This may explain why there is no evidence of the other January effect in advanced emerging markets.
3.4.3. The day of the week effect
To examine the day of the week effect, daily logarithmic returns for each index are regressed on five dummy variables for each day of the week, excluding the intercept term. These dummy variables are used in order to find any differences in returns for different days of the week. The regression equation is specified as follows:
= , + (3.5) 5 𝑟𝑟𝑡𝑡 ∑𝑖𝑖=1 𝛼𝛼𝑖𝑖𝐷𝐷𝑖𝑖 𝑡𝑡 𝜀𝜀𝑡𝑡
112 | Page
where is the logarithmic return of a given market index over the period
𝑡𝑡 t-1 to t, is the𝑟𝑟 average return of day i, and is the dummy variable which is
𝑖𝑖 𝑖𝑖 equal to 𝛼𝛼1 for day i and zero otherwise. Therefore,𝐷𝐷 there are five dummy variables for five trading days in a calendar week and is the random error term. The
𝑡𝑡 ordinary least squares estimator has been used 𝜀𝜀to estimate equation (3.5) where the ARCH LM test (Engle, 1982) cannot reject the null hypothesis of constant variance of error term across time. Where the ARCH LM test does provide evidence of time-varying𝜀𝜀 error variance, the GARCH model of the following form is used:
= + + (3.6) 2 ℎ𝑡𝑡 𝜆𝜆1 𝜆𝜆2𝜀𝜀𝑡𝑡−1 𝜆𝜆3ℎ𝑡𝑡−1 In equation (3.6), is the conditional error variance for day t, and ,
𝑡𝑡 1 2 and are GARCH modelℎ parameters. Equations (3.5) and (3.6) are estimatedλ λ
3 usingλ the maximum likelihood estimator. To examine the seasonal patterns of volatility in each market, the return volatility series (h ) predicted from equation
t (3.6) has been regressed on five dummy variables for calendar� days as indicated in equation (3.7):
h = D , + u (3.7) 5 �t ∑i=1 βi i t t In equation (3.7), is the average variance of the given stock index in day
𝑖𝑖 i. The null hypotheses of𝛽𝛽 = and = (for i j) are tested using the
i j i j Wald test to examine the presenceα α of dailyβ seasonalityβ ∀ in≠ returns and volatilities, respectively.
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As reported in Table 3.5, different days of the week tend to have different average returns, confirming the day of the week effect in advanced emerging markets. Mondays tend to have negative and lower than average returns in six out of the nine advanced emerging markets. In fact, Hungary is the only country with a positive and statistically significant Monday return. In contrast, Fridays tend to have positive and higher than average returns in eight out of nine countries with five of them being statistically significant. The average mean Friday return for the entire sample is 0.110% compared to -0.008% for Mondays and 0.062% for the three remaining days.
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Table 3.5: Results for the day of the week effect (denominated in local currency of each country) The table shows the regression results for equation (3.5) for Mondays and Fridays for all advanced emerging countries. PP is the proportion of positive daily excess returns in the entire sample period and p-value (given in parenthesis) is the significance of a binomial test to show if the proportion is statistically different from 50% for all days. ** indicates significance at the 0.01 level and * indicates significance at the 0.05 level. Returns denominated in local currency of each country Country Years Monday Friday All Remaining Days All Days
PP PP PP pp
Brazil 20 -0.0870 0.446 0.1748 0.508 0.0690 0.493 0.486 𝛼𝛼𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝛼𝛼𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹𝐹 𝛼𝛼𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝐷𝐷𝐷𝐷𝐷𝐷𝐷𝐷 (0.0891) (0.0001**) (0.0003**) (0.0220*) (0.0103*) (0.0016**)
Czech Republic 21 0.0421 0.478 0.0053 0.461 0.0437 0.478 0.475
(0.3222) (0.0091**) (0.8966) (0.0009**) (0.0007**) (0.0000**)
Hungary 23 0.1055 0.477 0.0646 0.487 0.0193 0.472 0.476
(0.0264) (0.0065**) (0.1585) (0.0159*) (0.0001**) (0.0000**)
Malaysia 28 -0.0948 0.415 0.1141 0.523 0.0679 0.490 0.481
(0.0151) (0.0000**) (0.0002**) (0.0043**) (0.0046**) (0.0001**)
Mexico 25 -0.0431 0.443 0.1166 0.489 0.1143 0.499 0.486
(0.2585) (0.0000**) (0.0002**) (0.0160*) (0.0127*) (0.0008**)
Poland 20 0.0767 0.498 0.0368 0.490 -0.0082 0.489 0.491
(0.1738) (0.0245*) (0.4295) (0.0196*) (0.0065**) (0.0046**)
South Africa 41 -0.0029 0.468 0.0449 0.478 0.0984 0.503 0.491
(-0.1) (0.0002**) (0.0643) (0.0020**) (0.0085**) (0.0015**)
Taiwan 26 -0.0322 0.452 0.1100 0.501 0.0064 0.468 0.472
(0.5823) (0.0000**) (0.0159*) (0.0216*) (0.0000**) (0.0000**)
Turkey 26 -0.0340 0.448 0.3205 0.508 0.1507 0.468 0.472
(0.66719) (0.0000**) (0.0000**) (0.0184*) (0.0000**) (0.0000**)
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The Wald test has been conducted for each country to check if stock returns on Mondays and Fridays are statistically different from other days of the week. In unreported results, the first null hypothesis has been rejected at the 5% significance level for six out of nine advanced emerging markets, thus confirming the existence of the day of the week effect. In particular, Mondays tend to have statistically lower returns in these countries, which confirms the study by Cross
(1973) in the US stock market. In most cases, the difference between Monday returns and Friday returns are more significant than other pairs, which implies that
Fridays tend to have higher returns, confirming the study by Lakonishok and Levi
(1982) and the existence of the Friday effect. In South Africa, however,
Wednesdays and Thursdays tend to have statistically higher than average returns in comparison to Mondays (with p-value of 0.0001 and 0.0045, respectively). In
Poland, Monday returns tend to be slightly higher than Tuesday returns with a p- value of 0.0893. This study finds no evidence of the day of the week effect in
Hungary and the Czech Republic where the differences in mean returns across different days of the week are not statistically significant even at the 10% level.
The ARCH LM test provides evidence of heteroscedasticity at the 1% significance level for Hungary, Poland and Turkey. For these markets, the return volatility has been predicted from a GARCH (1, 1) model and then regress predicted volatility on dummy variables for calendar days as delineated in equation (3.7). The null hypothesis of = cannot be rejected at the 5%
i j significance level for any of these threeβ maβrkets. Consequently, there is no evidence to confirm the existence of any seasonality in volatility of daily returns.
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Several explanations of the day of the week effect are put forward in the context of developed markets. Campbell and Hentschel (1992) claim that stock returns are higher on Fridays because of higher volatility of returns on Fridays relative to Mondays. However, this explanation does not hold for advanced emerging markets since the higher Friday return is accompanied, on average, by a lower volatility in the sample.
Prior studies on emerging markets suggested the negative Tuesday effect in addition to higher than average returns on Fridays and lower than average returns on Mondays (Agrawal & Tandon, 1994; Brooks & Persand, 2001). The most important reason underlying the negative Tuesday effect is the time difference between the US stock markets and Eastern countries (Aggarwal & Rivoli, 1989).
However, this study does not confirm a negative Tuesday effect in any of the countries studied, despite analysing countries that have different time zones, including Malaysia and Taiwan, which has a 13-hour time difference with the US stock market.
3.4.4. Holiday effect
The ordinary least squares regression has been used for each index where daily logarithmic returns are dependant variable. To control for different average returns on different days of the week, six dummy variables have been used as follows:
r = + D , + D , + D , + D , +
𝑡𝑡 α0 αTuesday 1 t αWednesday 2 t αThursday 3 t αFriday 4 t D , + D , + (3.8)
αPre−Holiday 5 t αPost−Holiday 6 t ε𝑡𝑡
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where the first four dummy variables have been used to control for different returns in different days of the week. These dummy variables are equal to one for the specified day and zero if otherwise. For instance, for the return of
Wednesday, is equal to one and , and are equal to zero. Variable
2 1 3 4 5 will be equal𝐷𝐷 to one for days before𝐷𝐷 national𝐷𝐷 holidays𝐷𝐷 of the country and zero𝐷𝐷 otherwise and variable will be equal to one for days after national holidays of
6 the country and zero otherwise.𝐷𝐷 The following two null hypotheses have been tested separately in order to investigate the existence of pre-holiday and post- holiday effects respectively: : = 0 and : = 0.
01 𝑝𝑝𝑝𝑝𝑝𝑝−ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 02 𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝−ℎ𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜𝑜 Rejecting the first (second) null𝐻𝐻 hypothesis𝛼𝛼 indicates that after𝐻𝐻 correcting𝛼𝛼 for daily seasonality, the average incremental return for days prior to (after) holidays is zero. Therefore, the results of this study will test for the existence of pre-holiday and post-holiday effects in these countries.
Table 3.6 shows the descriptive statistics of returns on days before and after holidays as well as all remaining days in the year. The number of holidays in the sample period varies between 10 for Mexico and 23 for Taiwan per year. In six out of nine countries, the mean returns for days before holidays are higher than the remaining days. Moreover, the mean returns are higher for days after holidays in all advanced emerging countries, providing formative evidence that supports the existence of a strong post-holiday effect, as observed by Lakonishok and
Smidt (1988). The average mean returns on days after holidays are 13 times higher than the average returns on days before holidays and 20 times higher than the remaining days in advanced emerging markets.
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Table 3.6: Descriptive statistics of pre-holiday and post-holiday returns The table shows returns on advanced emerging markets Before and After Holidays denominated in the local currency of each country. Median and average daily stock returns are presented in percentage terms. Note that 11 days in Malaysia and 15 days in Poland are both before and after holidays. Returns Returns After Returns On All Days that are both Before Holiday Other Days before and after holidays Holiday Brazil Median 0.14213 0.31190 0.06413
Mean 0.30684 0.36149 0.03668
Observation 211 211 4508
Czech Republic Median 0.01483 0.07613 0.01275
Mean 0.25495 0.28309 0.01672
Observation 178 178 4623
Hungary Median 0.01090 0.10099 0.03446
Mean 0.09994 0.30951 0.04898
Observation 177 177 4649
Malaysia Median 0.31713 0.11352 0.03276 0.22700 Mean 0.20098 0.28009 0.02768 0.31583 Observation 228 228 4415 11 Mexico Median 0.01053 0.16859 0.08817
Mean 0.08471 0.17153 0.08878
Observation 163 163 4684
Poland Median 0.00947 0.38600 0.02521 0.01202 Mean 0.04608 0.38797 0.00298 0.12233 Observation 182 182 4704 15 South Africa Median 0.01588 0.04996 0.07254
Mean 0.00234 0.24467 0.07381
Observation 77 77 4892
Taiwan Median 0.00646 0.00908 0.00452
Mean 0.14177 0.02645 0.01571
Observation 168 168 4624
Turkey Median 0.29328 0.76625 0.10101
Mean 0.07691 0.32658 0.15200
Observation 154 155 6240
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Table 3.7 shows the regression results for equation (3.8). The pre-holiday effect is persistent in Brazil and the Czech Republic in which the first null hypothesis can be rejected at the 5% level. This effect also exists in Malaysia at the 10% significance level. The post-holiday effect is statistically significant at the
5% level in six countries. The effect is weak in Mexico with a statistical significance of 10%. However, the post-holiday effect does not exist in Taiwan and Turkey. Table 3.7 also shows that in five out of nine countries, Monday’s returns are statistically different from other days, confirming the results of the previous section.
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Table 3.7: Regression results for pre-holiday and post-holiday effect for advanced emerging markets (denominated in the local currency of each country) Coefficients have been estimated using equation (3.8) for all advanced emerging markets separately. Note that to calculate a return of a specified day, the related coefficients should be multiplied by 100. For instance, the return of a Wednesday in Brazil which is after a national holiday is (- 0.1281+0.2385+0.3633) = 0.4737%. ** indicates significance at the 0.01 level and * indicates significance at the 0.05 level. Brazil Czech Republic Hungary Malaysia Mexico Poland South Africa Taiwan Turkey Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Constant -0.001281 0.000139 0.000977 -0.001265 -0.000587 0.000553 -0.000134 -0.000315 -0.000469
(0.015*) (0.757) (0.049*) (0.001**) (0.123) (0.320) (0.642) (0.542) (0.674)
Tuesday 0.001722 -0.000052 -0.000823 0.001377 0.001372 -0.001221 0.000389 -0.000199 0.001142
(0.020*) (0.934) (0.231) (0.008**) (0.009**) (0.114) (0.334) (0.783) (0.313)
Wednesday 0.002385 -0.000064 -0.000779 0.002246 0.001840 -0.000994 0.001604 0.000912 0.002181
(0.001**) (0.918) (0.258) (0.000**) (0.001**) (0.199) (0.000**) (0.205) (0.037*)
Thursday 0.001307 0.000677 -0.000975 0.001842 0.001929 0.000014 0.001178 0.000289 0.002780
(0.076) (0.279) (0.157) (0.000**) (0.000**) (0.985) (0.003**) (0.688) (0.007**)
Friday 0.002794 -0.000385 -0.000401 0.002220 0.001752 -0.000334 0.000550 0.001322 0.003843
(0.000**) (0.541) (0.564) (0.000**) (0.001**) (0.667) (0.175) (0.067) (0.000**)
Before Holiday 0.002602 0.002551 0.000298 0.001466 0.000141 0.000188 0.000030 0.001401 -0.001180
(0.024*) (0.017*) (0.802) (0.053) (0.881) (0.883) (0.970) (0.263) (0.667)
After Holiday 0.003633 0.002941 0.002435 0.002420 0.001826 0.003544 0.002828 0.000110 0.002176
(0.002**) (0.006**) (0.039*) (0.001**) (0.053) (0.005**) (0.000**) (0.930) (0.420)
Adjusted R-square 0.004886 0.001798 0.000238 0.004613 0.002236 0.001319 0.002476 0.000315 0.001854
F-statistic 5.03454 2.54389 1.228538 6.381988 3.357993 2.11867 5.316813 1.341396 3.026956
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This study finds that the post-holiday effect is more prevalent than the pre- holiday effect in advanced emerging markets, as opposed to prior studies that found evidence that the pre-holiday effect is more dominant (Ariel, 1990; Easton,
1990). The results of this study show that the holiday effect is persistent and is not related to or caused by the day of the week effect. The existence of the holiday effect in advanced emerging markets supports the argument that the holiday effect is an international phenomenon and is independent from the individual market characteristics of each country (Kim & Park, 1994). In fact, the effect is not driven by institutional investors because it persists in most developed and emerging markets with different proportions of institutional investors. The effect is also independent of the characteristics of the holidays because there are different holidays in the sample data for each country; for instance, Christmas and Boxing
Day, Chinese New Year, Easter and Muslims’ religious holidays.
The inventory adjustment hypothesis, also called the “lore of the street”, is one of the most well-known explanations about the pre-holiday effect introduced by Ariel (1990). According to this hypothesis, investors tend to close their short- selling positions before the market closes on days preceding the holidays.
However, as identified in table 3.1, short selling is not feasible, or is limited to the liquid stocks in advanced emerging markets. This limited accessibility to short selling may be the reason for having a relatively weaker pre-holiday effect compared to developed markets.
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3.4.5. The week of the year effect
In examining the week 44 effect, the methodology of Levy and Yagil (2012) has been applied. Weekly logarithmic market returns data have been obtained for the entire sample, with the first week commencing January 1 and ending January 7.
The following time series regression model has been used to test the week 44 effect:
= , + (3.9) 53 𝑟𝑟𝑡𝑡 ∑𝑤𝑤=1 𝛼𝛼𝑤𝑤𝐷𝐷𝑤𝑤 𝑡𝑡 𝜀𝜀𝑡𝑡 where is the dummy variable that is equal to one during week w and
𝑤𝑤 zero if otherwise.𝐷𝐷 Note that equation (3.9) consists of 53 dummy variables and the
53rd week contains fewer than seven days. is the average return of a given
𝑤𝑤 stock index for week w and is the error term.𝛼𝛼
𝜀𝜀 The ordinary least squares estimator has been used to estimate equation
(3.9) where the ARCH LM test (Engle, 1982) cannot reject the null hypothesis of constant variance of error term across time. Where the ARCH LM test does
𝑡𝑡 provide evidence of time-varying𝜀𝜀 error variance, the GARCH model of the following form is used:
= + + (3.10) 2 ℎ𝑡𝑡 𝜆𝜆1 𝜆𝜆2𝜀𝜀𝑡𝑡−1 𝜆𝜆3ℎ𝑡𝑡−1 In equation (3.10), is the conditional weekly error variance, and ,
𝑡𝑡 1 2 and are GARCH model ℎparameters. Equations (3.9) and (3.10) are estimated𝜆𝜆 𝜆𝜆
3 using𝜆𝜆 the maximum likelihood estimator. To examine the seasonal patterns of volatility in each market, the conditional volatility series (h ) predicted from �t
123 | Page equation (3.10) has been regressed on 53 dummy variables for calendar weeks as indicated in equation (3.11):
h = D , + (3.11) 5 �t ∑p=1 β𝑝𝑝 𝑝𝑝 t 𝑢𝑢𝑝𝑝 where is the average variance of the given stock index in week p. The
𝑝𝑝 following two𝛽𝛽 null hypotheses have been tested using the Wald test to identify possible seasonal patterns in returns and volatilities, respectively: H : =
0 i j and H : = , for j i. α α
0 βi βj ∀ ≠ Table 3.8 summarises the results of the analysis for each country. The mean return of the 44th week of the year is higher than average returns in all remaining weeks. More specifically, the average mean return for week 44 is more than 7 times higher than the returns for all remaining weeks in advanced emerging markets. Week 44 ranks in the top four weeks during the year for eight out of nine markets. It should be noted that the data includes one of an outlier in all countries in the sample, the 44th week of the year 2008. However, even after removing this outlier, the week 44 effect persists across seven out of nine advanced emerging markets. This research finds no evidence of the week 44 effect in the Czech
Republic and Malaysia once the outlier is removed from the sample. Figure 3.1 shows the average returns for 44th week of the year compared with all remaining weeks for advanced emerging markets.
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Table 3.8: Results for week 44 effect for advanced emerging markets (denominated in local currency of each country) The table shows the regression results for equation (3.9), which examines abnormal returns in week 44 for all advanced emerging countries. All returns have been denoted in percentage terms. PP is the proportion of positive weekly excess returns in the entire sample period and p-value (given in parenthesis) is the significance of binomial test to show if the proportion is statistically different from 50% for all weeks. The return rank column has been calculated by sorting the weeks of the year based on weekly returns. ** indicates significance at the 0.01 level and * indicates significance at the 0.05 level. Returns denominated in local currency of each country Country Years Week 44 All Remaining Weeks All Weeks
PP Return Rank PP PP
Brazil 20 3.0477 0.75 1 0.2279 0.51 0.509 𝛼𝛼𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊44 𝛼𝛼𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅𝑅 𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊𝑊 (0.0001**) (0.0148*) (0.0240*) (0.0208*)
Czech Republic 21 1.5101 0.45 1 0.1419 0.53 0.531
(0.0455*) (0.1602) (0.0025**) (0.0030**)
Hungary 23 2.1621 0.65 2 0.1903 0.50 0.500
(0.0067**) (0.0584) (0.0229*) (0.0230**)
Malaysia 28 0.3065 0.43 22 0.2153 0.51 0.510
(0.6031) (0.1133) (0.0137*) (0.0150**)
Mexico 25 1.6350 0.72 4 0.3862 0.50 0.506
(0.0088**) (0.0143*) (0.0219*) (0.0197*)
Poland 20 1.9047 0.60 4 0.0552 0.51 0.516
(0.04236*) (0.1201) (0.0165*) (0.0147*)
South Africa 41 1.4621 0.68 2 0.3117 0.50 0.505
(0.00158**) (0.0080**) (0.0172*) (0.0156*)
Taiwan 26 1.5670 0.69 4 0.0640 0.51 0.512
(0.0615) (0.0233*) (0.0176*) (0.0141*)
Turkey 26 2.5955 0.50 4 0.6944 0.48 0.477
(0.0308*) (0.1550) (0.0050**) (0.0051**)
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Figure 3.1: The average weekly returns in advanced emerging markets (%) 3.50 week 44
3.00 Remaining
weeks 2.50
2.00
1.50
Weekly Weekly Returns (%) 1.00
0.50
0.00
Country
The Wald test has been used to test the null hypothesis that mean returns are equal across all weeks of the year. In unreported results, the null hypothesis is rejected for all countries at the 5% significance level, confirming the existence of weekly seasonality in returns. In particular, the 44th week is among the top 10% when sorting weeks based on average returns for eight out of nine advanced emerging markets. For Malaysia however, the week 44 effect is not statistically significant and instead, the first week of February (week 5) and the second half of
December (weeks 51 and 52) have statistically higher than average returns for the remaining weeks. For instance, the average return of weeks 5, 51 and 52 are statistically higher than week 37 with a p-value of 0.01 or less.
The ARCH LM provides evidence of heteroscedasticity in weekly returns only for South Africa at the 1% significance level. For South Africa, the conditional return volatility is obtained from a GARCH (1, 1) model and then equation (3.11) is estimated to examine seasonality in weekly return volatility. In
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Figure 3.2, the coefficients of weekly dummy variables in equation (3.9) and seasonal volatility equation (3.11) has been plotted for South Africa. This figure shows that the variance of returns for the 44th week of the year is highest among all weeks of the calendar year, indicating that the 44th week is riskier than other weeks. The Wald test result implies that the difference is statistically significant.
Thus, a higher mean return in week 44 is accompanied by a higher return volatility in the case of South Africa, but not for the other countries in the sample.
Figure 3.2: The conditional volatility and mean returns for individual weeks during the year in South Africa.
Note that the X-axis is the number that has been assigned to weeks in the calendar year, the Y-axis on the right is the beta estimated in equation (3.11) and the Y-axis on the left denotes the returns in percentage format calculated from equation (3.9). The figure shows the average variance of returns for different weeks. The shaded area shows the conditional volatility and mean return for week 44.
Kamstra et al. (2003) argue that the abnormal returns for the 44th week of the year may be caused by the SAD effect. According to the SAD effect, daylight has a great effect on an investor’s mood, which can cause behavioural changes in the markets (Lo & Wu, 2010). Investors tend to hold portfolios of safer assets
127 | Page during the northern hemisphere fall and prefer riskier choices during the winter
(Dolvin et al., 2010). This movement of capital at the end of the fall season may have caused abnormal returns during the 44th week of the year.
The sample used for this study consists of nine advanced emerging markets with different daylight saving time practices. Daylight saving finishes on the last Sunday of October in the Czech Republic, Hungary, Mexico, Poland and
Turkey. In Brazil, the daylight saving system starts on the third Sunday of
October. Note that Malaysia, South Africa and Taiwan do not have a daylight saving system. South Africa is located in the southern hemisphere. The evidence of the week 44 effect in South Africa can be justified in terms of higher return volatility rather than by investors’ seasonal mood swings. Table 3.8 shows no evidence of abnormal returns during the 44th week of the year in Malaysia, suggesting the irrelevance of the SAD hypothesis in a country located in the tropics.
3.5.Robustness check
As a robustness check for the results, the same analysis has been conducted using the US dollar denominated returns. This analysis allows for an examination of seasonal anomalies from the perspective of international investors, as it controls for the effect of exchange rates.
The reported results in the appendix show that the US dollar denominated returns confirm the existence of the December effect in all advanced emerging markets. The December mean returns in advanced emerging markets ranges
128 | Page between 2.43% for Mexico and 6.23% for Turkey. The January returns ranges between -0.45% for Brazil and 6.32% for Turkey while the average February to
November monthly returns ranges between -0.24% for Poland and 1.13% for
Mexico. The US dollar denominated returns also confirm the existence of the day of the week effect throughout all advanced emerging markets where Friday returns tend to be higher and Monday returns tend to be lower. The average
Monday returns ranges between -0.1371% for Malaysia and 0.1095% for Hungary while the average Friday returns ranges between 0.0284% for the Czech Republic and 0.2002% for Turkey. This research also examines the holiday effect using the
USD denominated returns. The results show that the post-holiday effect is persistent in seven out of nine advanced emerging markets, confirming the results of this study when using the local currency denominated returns. However, there is no post-holiday effect when US dollar denominated returns are used in Taiwan and Turkey. The analysis of the US dollar denominated returns also suggests the existence of the week of the year effect in all advanced emerging markets. The
44th week of the year has higher average returns than other weeks of the year in all advanced emerging markets. The average returns of the 44th week of the year ranges between 0.04668% for Malaysia and 3.6924% for Brazil while for other weeks during the year, this number ranges between 0.0164% for Poland and
0.2545% for Mexico.
3.6.Summary
This chapter studied the presence of five seasonal anomalies across nine advanced emerging markets. This study makes two major contributions. First, it provides a
129 | Page re-examination of market efficiency of advanced emerging markets by testing for the significance of five seasonal anomalies. This re-examination is important, in light of recent evidence that conflicts with conventional wisdom and suggests that emerging markets are “at least as efficient as developed markets” (Griffin et al.,
2010, p. 3260). Second, the sample used in this study is a unique laboratory for conducting out-of-sample tests for five seasonal anomalies and examining the hypotheses that have been proposed for these predictable patterns in returns. It has been suggested that many of the seasonal anomalies identified in developed markets are sample-specific and not pervasive across different time periods (for example, see Zhang and Jacobsen (2012) and Marshall and Visaltanachoti
(2010)).
This research finds no evidence to support the presence of the other
January effect in advanced emerging markets, while supporting the existence of the month of the year effect, the day of the week effect, the holiday effect and the week of the year effect. This study has documented the evidence that the day of the week effect (high stock returns on Fridays and low stock returns on Mondays), the week 44 effect and the holiday effect exist in advanced emerging markets.
However, by testing the month of the year effect, this study shows that stock returns in December tend to be higher than returns in other months of the year in advanced emerging markets as opposed to the existence of the January effect in developed markets. Finding no seasonal patterns/less-dominant seasonal patterns in stock returns in advanced emerging markets, shows the weak form efficiency of the markets and can support the argument of Griffin et al. (2010) that emerging markets are at least as efficient as developed markets. However, it can also be
130 | Page interpreted as different behaviour of investors in these markets compared to developed markets. This study finds similar results for data denominated in both
US dollars and the local currency of each country. For brevity, only the results from local currency denominated returns are reported here. In general, the results from US dollar denominated returns are similar to those obtained from returns in local currency, suggesting the absence of seasonality in exchange rates of these countries. It also confirms that the extreme events such as change in the exchange rate regime has not introduced seasonality in these markets and thus, have not affected the results.
The results show that the January effect and the other January effect are not pervasive in advanced emerging markets, thus refuting tax-loss selling and window dressing explanations. Having a low proportion of managed funds may have been the key reason for the absence of the January effect and the other
January effect in advanced emerging markets. This study also shows that volatility patterns cannot be the only reason for the day of the week effect as this chapter has documented the existence of this effect while finding no evidence for having different volatility for days of the week. In addition, the results of this study show that having a different time zone compared to the US stock market cannot be the only reason for a negative Tuesday effect. This research also documents the strong holiday effect in these markets, which shows that this effect may not be related to the characteristics of the national holidays of each country.
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132 | Page
Chapter 4 : Limits to arbitrage and the MAX effect in
emerging markets
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4.1.Introduction
Traditional asset pricing models assume that investors hold well-diversified portfolios and hence diversifiable risks are not priced. Recently however, many studies suggest that a large proportion of investors do not hold diversified portfolios (Calvet, Campbell, & Sodini, 2007; Odean, 1999). Goetzmann and
Kumar (2008) argue that under-diversification happens more among younger, less-educated, low-income individual investors. Studies have proposed many reasons why investors do not hold well-diversified portfolios. According to Sun and Yan (2003), investors’ preferences may affect their decisions in forming a portfolio. They argue that investors are attracted to lottery type assets, which are assets with a low probability of extremely high returns. Mitton and Vorkink
(2007) examine skewness-seeking investors and find that these investors generally hold under-diversified portfolios. They also claim that under-diversified portfolios are significantly more positively skewed and argue that this does not happen coincidently (Mitton & Vorkink, 2007). Similarly, Chunhachinda, Dandapani,
Hamid, and Prakash (1997) find evidence that investors change the composition of their optimal portfolio to gain positive skewness. Investors holding undiversified and not mean-variance optimal portfolios may cause diversifiable characteristics of stocks such as skewness and idiosyncratic volatility to be priced in stock returns.
While the mean-variance framework of portfolio optimisation assumes investors are only interested in the first two moments of the returns distributions, evidence suggests that investors prefer stocks with positive skewness even if these
134 | Page assets are riskier and have lower expected returns (Kumar, 2009). This preference is consistent with the cumulative prospect theory of Tversky and Kahneman
(1992) which states that investors make errors in probability weighting and overvalue the low probability of high returns. Consequently, investors gain utility based on their optimistic view of future returns (Brunnermeier, Gollier, & Parker,
2007). This tendency of investors toward lottery type assets can result in such assets becoming over-priced and thus generating low expected future returns
(Barberis & Huang, 2008).
Inspired by investors’ tendency of buying stocks with positive skewness,
Bali et al. (2011) introduced the MAX variable, which is the maximum daily return of stocks over the previous calendar month. The maximum daily return can be an indicator of positive skewness or lottery like pay off, especially for unsophisticated individual investors. Bali et al. (2011) sorted stocks in the US stock markets over the period 1926-2005 into decile portfolios based on the MAX variable and found that, consistent with their expectation, stocks with highest maximum daily returns over the previous month underperformed stocks with lowest maximum daily return by more than 1% per month. The relationship between MAX and stock returns is statistically significant and robust after controlling for size, book to market ratio, momentum, liquidity, short-term reversals, idiosyncratic volatility and skewness (Bali et al., 2011).
Bali et al. (2011) argued that the existing risk factors are not adequate to explain the return difference between high and low MAX portfolios. They provided two possible reasons for the existence of the MAX effect. First, the extreme returns in high MAX portfolios could be caused by the over-reaction of
135 | Page investors to firm-specific good news. They argued that such over-reaction would cause the stocks to have lower stock returns in the subsequent months. However, this explanation is not consistent with two propositions in the literature. First,
Daniel, Hirshleifer, and Subrahmanyam (1998) argued that investors tend to under-react rather than over-react to the firm-specific good news; and Bernard and
Thomas (1989) reported evidence of a post-earning announcement drift, in which stock prices continue to drift in the same direction following unexpected earnings announcements. Second, the prices of high MAX portfolio stocks could be affected by poorly diversified investors who prefer stocks with lottery like pay offs. The extra demand for such stocks would cause the stock prices to be higher than their intrinsic value and thus, these stocks have lower expected returns in the future (Bali et al., 2011). Zhong and Gray (2016) constructed a mispricing index for Australian stocks based on seven anomalies and argue that the MAX effect is caused by mispricing. They claim that mispricing is mostly among overpriced stocks; that is, stocks with the highest MAX over the previous month.
Despite the magnitude and importance of the MAX effect in explaining stock returns, only few studies have comprehensively examined this effect in international markets. Walkshäusl (2014) examined the European stock markets and found a strong MAX effect in stock returns. Annaert, De Ceuster, and
Verstegen (2013) also studied the MAX effect in 13 European stock markets and found that the MAX effect is persistent in equal weighted, and not value weighted, portfolios. Nartea et al. (2014) found a pervasive MAX effect in the
South Korean stock market from 1993 to 2008. However, Chee (2012) examined the Japanese markets and found no evidence of the MAX effect. Nartea and Wu
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(2014) found evidence of the MAX effect in the Chinese market for the holding period of three to six months, but not for the one-month holding period. Cheon and Lee (2014) provided global evidence that after controlling for MAX, the idiosyncratic volatility puzzle raised by Ang, Hodrick, Xing, and Zhang (2006) disappears. In fact, after controlling for MAX, the negative relation between idiosyncratic volatility and returns of Ang et al. (2006) becomes positive. Cheon and Lee (2014) argued that, while investors prefer stocks with positive skewness, they require a premium for holding stocks with high idiosyncratic volatility.
Extant studies of the MAX effect have argued that this anomaly is explained by behavioural factors rather than risk (Zhong & Gray, 2016). The behavioural view of asset pricing anomalies relies on two assumptions: investor irrationality that results in mispricing, and limits to arbitrage, which prevent that mispricing from being eliminated by sophisticated investors (Barberis & Thaler,
2003). The presence of irrational investors and limits to arbitrage are both magnified in emerging markets; therefore, an examination of the existence of the
MAX effect in emerging markets provides an important contribution to the literature. There are a lower proportion of managed funds, and thus a higher proportion of unsophisticated investors in emerging markets. These unsophisticated investors may be more susceptible to irrationality, given that individual investors in emerging markets also tend to be less educated compared to those in developed markets (Voronkova & Bohl, 2005). Emerging markets also present significant limits to arbitrage to international investors, given risks associated with government expropriation, high transaction costs, lack of information transparency, low investor protection (Carrieri, Chaieb, & Errunza,
137 | Page
2013), higher corruption (Switzer & Tahaoglu, 2015) and weaker shareholders’ rights (López de Silanes, La Porta, Shleifer, & Vishny, 1998). In addition,
Bekaert, Erb, Harvey, and Viskanta (1997) claimed that the political risk as well as the currency risk is priced into many emerging markets.
Emerging markets also have unique volatility characteristics that provide an interesting setting to examine the efficacy of the MAX effect. Bekaert, Erb,
Harvey, and Viskanta (1998) provide evidence that, apart from higher volatility, stock returns in emerging markets exhibit higher deviation from normality compared to developed markets. Aggarwal, Inclan, and Leal (1999) claim that emerging markets exhibit positive and higher skewness compared to developed markets. Bartram, Brown, and Stulz (2012) propose that market-specific risk factors may cause idiosyncratic volatility to be different in emerging and developed markets. They distinguish between “good” and “bad” volatility.
According to their explanation, good volatility is associated with stock market development, growth opportunities and R&D of the firm while bad volatility is caused by weak disclosure, poor accounting standards, noise trading, liquidity risk and political risk. They claim that the idiosyncratic volatility in emerging markets is more related to the “bad” volatility compared to developed markets. Fan, Opsal, and Yu (2014) showed that idiosyncratic volatility in emerging markets is different and has more impact on abnormal returns generated by other anomalies.
In addition, emerging markets comprise a lower proportion of institutional investors compared to developed markets (Voronkova & Bohl, 2005). Fong and
Toh (2014) suggested that the MAX effect is more pronounced among stocks that
138 | Page have a lower proportion of institutional ownership because individual investors are more willing to buy stocks with lottery-type pay offs (Kumar, 2009).
Despite the apparent pervasiveness of the MAX effect across developed markets, few studies have examined this phenomenon in the context of emerging markets. Emerging markets provide a unique laboratory to perform the out-of- sample test of the MAX effect given they exhibit considerable differences from developed markets in terms of stock returns, volatility and market characteristics.
Moreover, these differences create the opportunity to examine the applicability of existing behavioural explanations that have been proposed for the MAX effect. In particular, if the MAX effect is because of mispricing according to Zhong and
Gray (2016), emerging markets exhibit a relatively higher level of mispricing and limits to arbitrage compared to developed markets which can affect the pervasiveness of the MAX effect in these markets. This chapter studied the MAX effect across nine advanced emerging markets and compares the results with those reported in developed markets to propositions for this anomaly. This study also examined the relation between the MAX effect and the variables that affect the degree of limits to arbitrage presented in emerging markets. This helps us to understand the main sources of the MAX effect in stock returns.
The remainder of this chapter proceeds as follows. Section 2 explains the data used in this study. Section 3 discusses the methodology and presents the associated results. Section 4 provides a summary.
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4.2.Data
This study examined nine advanced emerging markets based on country classification of the Financial Times Stock Exchange; that is, Brazil, Czech
Republic, Hungary, Malaysia, Mexico, Poland, South Arica, Taiwan and Turkey
(FTSE, 2014). The required data has been collected from Datastream. More specifically, daily total returns for individual stocks and the December year-end market capitalization and book to market ratio of firms has been collected for the period from January 1995 to January 2015. The total returns index was used instead of price returns because they incorporate both capital gains and dividends.
Both listed and delisted stocks are included in the sample to avoid survivorship bias.
To reduce potential measurement errors in the data, the raw data collected from Datastream has been filtered following Griffin et al. (2010). In particular, their criteria have been used to identify non-common stocks and exclude them from the sample. Table 4.1 shows the number of stocks before and after filtering for non-common stocks. Taiwan lists 2254 (2244) stocks before (after) filtering non-common stocks, the most in the sample. On the other hand, Hungary lists the least number of stocks in the sample with 131 (128) stocks before (after) filtering non-common stocks. Daily logarithmic returns have been calculated from the total return indexes obtained from Datastream. To eliminate any possible error in the data, this study follows Griffin et al. (2010) to perform return filters. Any return greater that 200% and less than -200% has been excluded. In addition, if or
𝑡𝑡 are greater than 100% and (1 + )(1 + ) < 20%, then both 𝑟𝑟 and
𝑟𝑟𝑡𝑡−1 𝑟𝑟𝑡𝑡 𝑟𝑟𝑡𝑡−1 𝑟𝑟𝑡𝑡
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are set equal to zero, where is the log stock return on day t. In addition, the
𝑡𝑡−1 𝑡𝑡 𝑟𝑟first 90 trading days of stocks after𝑟𝑟 their listing date have been excluded from the sample to remove the effect of IPOs on stock returns.
As expected, liquidity is a major issue when analysing data related to emerging markets. To overcome this problem, the 30% liquidity filter applied by
Griffin et al. (2010) has been implemented. Using this filter, the percentage days with price changes during the year are measured and stocks with more than 70% zero return days are excluded from the sample for that year. Table 4.1 shows the number of stocks before and after performing a liquidity filter over the period
1995 to 2014. For instance, during 1995, Brazil had a total number of 226 listed stocks, but only 44 stocks remain after the liquidity filter is applied. While the liquidity filter does result in a significant number of observations being removed for some countries in some years, this filter is important, as including illiquid stocks would bias the results. The stocks that remain after the application of the
30% price change filter should be relatively large and liquid stocks; hence, they should represent the investable sub-set of stocks within each market.
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Table 4.1: Total number of listed stocks (Number Passing 30% Price Change Filter) The top of the table shows the total number of stocks in each country. It includes the stocks that have been delisted during the sample period. The data related to common equities have been used only and the uncommon equities have been excluded from the dataset. The bottom of the table shows the number of active common equities for each country each year and the number in the parentheses shows the number of stocks that have been traded more than 30% of days in the calendar year.
Brazil Czech Republic Hungary Malaysia Mexico Poland South Africa Taiwan Turkey
Total 1371 336 131 1261 690 1461 1297 2254 549 Common Equity 705 332 128 1210 656 1438 1295 2244 543 1995 226(44) 287(244) 44(22) 524(476) 283(159) 56(37) 586(143) 377(354) 207(184) 1996 252(52) 284(235) 38(20) 616(539) 281(157) 72(51) 637(354) 487(433) 228(204) 1997 250(56) 282(191) 47(23) 704(628) 284(164) 134(74) 659(441) 550(501) 256(230) 1998 268(56) 179(146) 49(29) 730(690) 261(146) 190(148) 738(472) 634(564) 276(256) 1999 301(84) 164(80) 58(38) 730(690) 258(140) 210(188) 715(463) 736(654) 278(264) 2000 294(84) 137(42) 57(40) 759(706) 225(134) 222(201) 653(402) 836(743) 306(276) 2001 271(71) 113(24) 53(32) 769(722) 205(115) 233(202) 564(291) 928(846) 297(283) 2002 271(68) 85(10) 50(34) 815(729) 172(103) 233(189) 485(256) 1052(956) 299(287) 2003 261(80) 70(9) 48(32) 863(766) 163(105) 219(186) 422(230) 1134(1040) 292(289) 2004 264(87) 64(12) 52(36) 909(835) 165(108) 237(196) 394(238) 1244(1118) 301(287) 2005 261(93) 54(17) 53(33) 954(855) 155(101) 271(231) 382(257) 1292(1188) 309(299) 2006 272(108) 41(11) 44(34) 964(892) 141(97) 327(247) 406(270) 1314(1232) 324(305) 2007 344(177) 34(14) 41(32) 966(900) 147(101) 469(285) 447(304) 1376(1264) 330(316) 2008 335(212) 32(14) 40(32) 919(758) 150(102) 459(339) 434(304) 1398(1311) 327(322) 2009 323(206) 30(13) 43(31) 895(725) 155(109) 453(376) 425(282) 1432(1350) 324(320) 2010 330(211) 26(14) 47(32) 889(773) 171(116) 593(435) 413(294) 1563(1425) 345(320) 2011 331(219) 29(14) 51(37) 878(766) 175(112) 778(552) 422(291) 1675(1532) 373(341) 2012 324(213) 28(15) 52(35) 858(737) 180(114) 881(631) 417(290) 1738(1587) 402(363)
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2013 321(215) 27(16) 50(38) 839(743) 179(124) 928(651) 411(302) 1774(1664) 419(394) 2014 308(220) 24(14) 49(34) 827(754) 170(124) 935(670) 410(296) 1835(1719) 423(407)
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To ensure that the portfolio returns are comparable with a representative benchmark, the equal weighted and value weighted market returns have been calculated for each country in the sample using the sample of stocks that pass the liquidity filters. Table 4.2 shows the descriptive statistics of market returns. For instance, Turkey has the average monthly returns of constructed equally (value) weighted indexes of 1.8837% (2.1964%) with p-value of 0.014 (0.053). The standard deviation of returns is highest in Turkey and lowest in South Africa.
Note that the skewness of returns is negative for all countries except for Poland’s equal weighted index. This implies that the average returns of tradable securities in advanced emerging markets after filtering for non-common stocks and illiquid stocks are negatively skewed and are not normal.
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Table 4.2: Descriptive statistics of monthly returns The table shows the average monthly returns of the constructed indexes and their p-value. The indexes have been constructed using active common stocks for each year for each country that have passed the liquidity filters. The rest of the table shows the skewness, the minimum, the maximum and the standard deviation of monthly returns of the constructed indexes.
Country Constructed Equal Weighted Index Constructed Value Weighted Index
Average Returns Standard Average Returns Standard Skewness Min Max Skewness Min Max (P-Value) Deviation (P-Value) Deviation Braz il 0.8241%(0.061) -0.9454 -31.17% 19.97% 0.0681 0.8691%(0.067) -0.6171 -28.59% 21.58% 0.0735 Czech Republic -0.4849%(0.236) -0.6640 -32.05% 21.70% 0.0634 0.1487%(0.746) -0.7746 -33.01% 19.46% 0.0712 Hungary -0.4503%(0.414) -0.9476 -47.51% 35.87% 0.0855 0.8931%(0.091) -0.9725 -46.88% 29.46% 0.0818 Malaysia -0.2708%(0.657) -0.0471 -46.07% 43.30% 0.0945 0.0980%(0.834) -0.3949 -32.23% 28.96% 0.0732 Mexico 0.6804%(0.073) -1.7295 -35.12% 15.35% 0.0587 0.5247%(0.242) -0.9897 -34.47% 19.10% 0.0694 Poland -0.3992%(0.392) 0.0364 -29.34% 27.11% 0.0723 0.1010%(0.820) -0.3703 -32.57% 27.28% 0.0749 South Africa 0.3685%(0.231) -1.6799 -32.25% 12.23% 0.0476 0.8269%(0.016) -1.0798 -29.97% 13.61% 0.0534 Taiwan -0.2633%(0.603) -0.0838 -28.28% 24.38% 0.0783 -0.1134%(0.802) -0.0751 -22.40% 26.27% 0.0703 Turkey 1.8837%(0.014) -0.2554 -48.87% 53.69% 0.1184 2.1964%(0.053) -0.6804 -99.14% 70.25% 0.1762
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4.3.Methodology and results
4.3.1. Univariate portfolio sorts
The univariate-sorted portfolios have been constructed using five different variables: MAX, size, value, momentum and liquidity. Stocks have been sorted into quintile portfolios ranked based on MAX so that stocks in quintile 1 (quintile
5) contains stocks with the lowest (highest) maximum daily returns over the past calendar month. For sorting based on size, stocks have been sorted into quintile portfolios ranked based on their market capitalization at the beginning of the year so that quintile 1 (quintile 5) contains the smallest (largest) stocks. In order to perform sorts based on value, stocks are sorted into quintile portfolios ranked on the book to market ratio of firms at the beginning of the year so that quintile 1
(quintile 5) contains stocks with the highest (lowest) book to market ratio. For sorting based on momentum, the twelve by twelve momentum strategy has been applied to follow the study of Jegadeesh and Titman (1993), which means the prior twelve month returns for the portfolio have been used as the formation period and then each portfolio is held for twelve months and rebalanced annually.
Quintile portfolios have been ranked so that quintile 1 (quintile 5) contains the worst (best) performing stocks. Finally, the percentage of zero return days over each calendar year has been used as a proxy for the liquidity of stocks (Lesmond,
Ogden, & Trzcinka, 1999). Note that stocks with less than 30% traded days during the year have been excluded for liquidity filtering purposes. This study uses one calendar year’s formation period and sorts the stocks into quintile portfolios
146 | Page ranked based on the liquidity proxy for the following year so that quintile 1
(quintile 5) contains stocks with lowest (highest) liquidity. The monthly equal weighted and value weighted returns are calculated for each of the portfolios described above. The MAX, Small minus Big market capitalization (SMB), High minus Low book to market ratio (HML), Momentum (MOM) and Liquidity (LIQ) factors have been constructed by calculating the difference in monthly returns of extreme portfolios.
Table 4.3 shows the results from equal weighted and value weighted univariate sorted portfolios based on the MAX effect. This chapter finds a strong
MAX effect in equal weighted portfolio sorts in all advanced emerging markets with the exception of Mexico. For instance, in the Czech Republic and Hungary, the monthly return difference of extreme portfolios is as high as 2.78% and 2.76% respectively, which is significant at a 1% level. Moreover, this study finds a strong MAX effect in value weighted portfolio sorts in five out of nine advanced emerging markets, significant at a 1% level, which are the Czech Republic,
Hungary, Malaysia, Poland and South Africa. Note that the MAX effect also exists in Brazil and Taiwan at a 10% level.
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Table 4.3: Average monthly returns of the portfolios sorted based on MAX Note that portfolio 1 is the portfolio with the lowest MAX on previous month while portfolio 5 is the portfolio with the highest MAX on previous month. The return of the “1-5” portfolio shows the average return of the MAX investment strategy. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Equal Weighted Value Weighted
Country 1 2 3 4 5 1-5 1 2 3 4 5 1-5
Brazil 1.135% 1.115% 0.764% 0.987% 0.235% 0.900% 1.203% 0.867% 0.837% 0.844% 0.059% 1.144%
0.002** 0.018* 0.160 0.062 0.666 0.025* 0.004** 0.089 0.187 0.239 0.932 0.076
Czech Republic 0.434% 0.138% 0.313% -0.364% -2.351% 2.784% 0.668% 0.496% 0.613% 0.261% -1.644% 2.312%
0.262 0.786 0.546 0.532 0.002** 0.000** 0.086 0.378 0.269 0.731 0.053 0.003**
Hungary 0.613% 0.829% 0.325% -0.421% -2.149% 2.761% 0.700% 1.092% 0.858% 0.405% -2.046% 2.746%
0.174 0.099 0.534 0.472 0.000** 0.000** 0.204 0.045* 0.160 0.538 0.013* 0.000**
Malaysia 0.242% 0.290% -0.029% -0.302% -1.487% 1.729% 0.283% 0.161% 0.048% -0.075% -1.086% 1.369%
0.599 0.604 0.964 0.667 0.054 0.000** 0.486 0.729 0.929 0.904 0.137 0.005**
Mexico 0.538% 1.177% 0.929% 0.991% 0.008% 0.530% 0.403% 0.880% 1.058% 0.879% -0.221% 0.624%
0.012* 0.002** 0.042* 0.049* 0.989 0.278 0.327 0.033* 0.024* 0.128 0.736 0.336
Poland 0.195% -0.005% -0.002% -0.551% -1.426% 1.621% 0.333% 0.472% -0.073% -0.556% -1.458% 1.791%
0.673 0.992 0.997 0.275 0.010** 0.000** 0.518 0.367 0.892 0.309 0.048* 0.004**
South Africa 1.056% 1.008% 0.678% 0.275% -1.129% 2.184% 1.307% 0.973% 0.646% 0.207% -1.334% 2.641%
0.000** 0.003** 0.040* 0.434 0.004** 0.000** 0.000** 0.009** 0.084 0.636 0.011* 0.000**
Taiwan 0.137% 0.158% -0.072% -0.477% -0.901% 1.038% 0.104% -0.097% 0.001% -0.487% -0.579% 0.684%
0.742 0.745 0.893 0.410 0.138 0.000** 0.784 0.832 0.999 0.372 0.331 0.073
Turkey 2.334% 2.575% 2.187% 1.748% 0.698% 1.636% 1.490% 2.968% 2.621% 2.215% 0.776% 0.714%
0.002** 0.002** 0.006** 0.028* 0.359 0.000** 0.080 0.003** 0.002** 0.015* 0.379 0.291
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In fact, the average monthly return difference between the lowest and the highest MAX portfolios is higher than 1.03% of Bali et al. (2011) in most cases, providing evidence that the MAX effect is higher among emerging markets compared to the US. These results support the idea of Carrieri et al. (2013),
Switzer and Tahaoglu (2015), López de Silanes et al. (1998) and others who claim that emerging markets exhibit a greater level of limit to arbitrage opportunities.
Note that most of the return variations between extreme portfolios are due to the underperformance of stocks in the highest MAX portfolio rather than the over performance of stocks in the lowest MAX portfolio. While exploiting any arbitrage opportunity regarding the MAX strategy requires investors to short-sell stocks in the highest MAX portfolio, short-selling is not permitted in Malaysia, not feasible in Brazil, Hungary and Poland and limited to the most liquid stocks in other advanced emerging countries (Charoenrook & Daouk, 2005). Therefore, it is more difficult for arbitragers to exploit the opportunity caused by the MAX effect in emerging markets compare to the US and other developed markets.
Table 4.4 to 4.7 show the results from equal and value weighted univariate sorted portfolio analysis based on size, book to market ratio, momentum and liquidity respectively. This research finds significant value effect among six (two) countries of the sample when analysing equal (value) weighted portfolios. The monthly average return difference between extreme value portfolios is highest in
Taiwan, which is 2.223% (1.067%) significant at 1% level when calculating the equally (value) weighted returns. Moreover, this study finds that the twelve by twelve momentum strategy is persistent in five out of nine advanced emerging markets. The monthly average return difference between extreme momentum
149 | Page portfolios is highest in South Africa (Czech Republic), which is 2.094% (1.987%) significant at 1% level when calculating the equally (value) weighted returns.
However, the results do not support the existence of Size effect and liquidity effect in advanced emerging markets. These results were expected considering that this study applies several liquidity-related filters to screen out illiquid stocks.
Note that given the vast problem of illiquidity among emerging markets, stocks with more than 70% of zero return days during the calendar year have been removed from the sample. These stocks are expected to belong to the companies with lowest market capitalization. Therefore, using liquidity filters may have caused the size and liquidity effects to disappear.
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Table 4.4: Average monthly returns of the portfolios sorted by Size Note that portfolio 1 consists of the smallest firms while portfolio 5 consists of the largest firms. The return of the “1-5” portfolio shows the average return of the investment strategy based on the market capitalization of individual firms. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Equal Weighted Value Weighted
Country 1 2 3 4 5 1-5 1 2 3 4 5 1-5
Brazil 1.195% 0.990% 0.786% 0.511% 0.583% 0.612% 1.221% 0.996% 0.808% 0.508% 0.942% 0.279%
0.018* 0.035* 0.110 0.308 0.194 0.079 0.032* 0.030* 0.106 0.321 0.058 0.527
Czech Republic -0.429% -1.368% -1.016% -0.294% 0.416% -0.845% -0.593% -1.328% -0.767% -0.222% 0.234% -0.827%
0.495 0.027* 0.095 0.577 0.369 0.171 0.383 0.042* 0.178 0.692 0.646 0.229
Hungary -1.576% -0.164% -0.378% 0.276% 0.797% -2.372% -2.040% 0.074% -0.360% 0.555% 0.753% -2.793%
0.011* 0.749 0.496 0.605 0.106 0.000** 0.001** 0.882 0.525 0.310 0.171 0.000**
Malaysia -0.464% -0.342% -0.419% -0.259% 0.098% -0.562% -0.477% -0.339% -0.408% -0.234% 0.202% -0.679%
0.541 0.611 0.499 0.662 0.845 0.194 0.525 0.612 0.511 0.690 0.655 0.172
Mexico 0.735% 0.544% 0.567% 0.889% 0.898% -0.164% 0.737% 0.512% 0.608% 0.879% 0.541% 0.196%
0.086 0.176 0.169 0.027* 0.033* 0.622 0.080 0.204 0.148 0.030* 0.251 0.628
Poland -0.531% -0.446% -0.613% -0.219% -0.031% -0.500% -0.545% -0.426% -0.610% -0.283% 0.207% -0.752%
0.355 0.387 0.225 0.659 0.948 0.214 0.338 0.405 0.226 0.562 0.679 0.109
South Africa 0.319% 0.054% 0.119% 0.558% 0.809% -0.490% -0.106% 0.080% 0.129% 0.617% 0.886% -0.993%
0.377 0.875 0.712 0.077 0.018* 0.103 0.760 0.815 0.690 0.051 0.013* 0.002**
Taiwan -0.147% -0.304% -0.357% -0.312% -0.251% 0.104% -0.183% -0.340% -0.369% -0.330% -0.036% -0.147%
0.775 0.573 0.514 0.565 0.611 0.747 0.722 0.528 0.501 0.543 0.936 0.682
Taiwan 2.121% 1.695% 1.779% 1.928% 2.062% 0.059% 1.940% 1.714% 1.823% 1.975% 2.209% -0.269%
0.005** 0.032* 0.028* 0.015* 0.009** 0.886 0.013* 0.032* 0.025* 0.012* 0.053 0.754
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Table 4.5: Average monthly returns of the portfolios sorted by book to market ratio Note that portfolio 1 consists of firms with the highest book to market ratio while portfolio 5 consists of firms with the lowest book to market ratio. The return of the “1-5” portfolio shows the average return of the value investment strategy, which is based on the book to market ratio of individual firms. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Equal Weighted Value Weighted
Country 1 2 3 4 5 1-5 1 2 3 4 5 1-5
Brazil 1.031% 1.005% 0.905% 0.250% 0.297% 0.733% 1.520% -0.027% 0.595% 0.796% 1.362% 0.158%
0.066 0.051 0.053 0.610 0.557 0.105 0.041* 0.965 0.280 0.169 0.010** 0.832
Czech Republic -0.824% -0.874% -0.020% -0.235% -0.916% 0.092% -0.370% -0.051% 0.299% 0.279% -1.138% 0.768%
0.246 0.098 0.969 0.581 0.135 0.899 0.630 0.931 0.596 0.548 0.095 0.317
Hungary -0.200% 0.281% 0.340% 0.314% -1.220% 1.019% -0.063% 1.276% 0.964% 0.270% -0.375% 0.311%
0.722 0.590 0.511 0.545 0.039* 0.089 0.924 0.032* 0.106 0.614 0.522 0.651
Malaysia -0.223% 0.246% 0.047% -0.328% -0.912% 0.688% 0.138% 0.460% 0.538% 0.356% -0.048% 0.186%
0.751 0.680 0.936 0.553 0.127 0.013* 0.844 0.413 0.289 0.446 0.917 0.662
Mexico 1.304% 0.801% 0.736% 0.566% 0.129% 1.176% 0.583% 0.953% 0.843% 0.992% 0.555% 0.028%
0.005** 0.081 0.059 0.134 0.739 0.001** 0.295 0.125 0.064 0.019* 0.252 0.957
Poland 0.463% 0.329% 0.184% -0.244% -1.090% 1.553% -0.316% 0.883% 0.761% 0.558% -0.102% -0.214%
0.433 0.498 0.707 0.626 0.033* 0.000** 0.679 0.103 0.158 0.308 0.854 0.747
South Africa 0.842% 0.639% 0.430% 0.199% -0.383% 1.225% 1.173% 1.017% 1.019% 0.778% 0.361% 0.812%
0.016* 0.036* 0.178 0.556 0.291 0.000** 0.012* 0.007** 0.005** 0.035* 0.356 0.017*
Taiwan 0.775% 0.175% -0.206% -0.533% -1.448% 2.223% 0.777% 0.364% 0.206% -0.107% -0.290% 1.067%
0.207 0.735 0.684 0.291 0.007** 0.000** 0.187 0.485 0.659 0.819 0.541 0.008**
Turkey 2.603% 2.433% 2.018% 1.596% 1.260% 1.343% 2.863% 3.102% 2.335% 2.162% 1.831% 1.032%
0.002** 0.003** 0.012* 0.046* 0.091 0.000** 0.001** 0.001** 0.008** 0.013* 0.058 0.127
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Table 4.6: Average monthly returns of the portfolios sorted by momentum Note that portfolio 1 consists of firms, which performed poorly over the previous year, while portfolio 5 consists of firms, which performed well over the previous year. The return of the “1-5” portfolio shows the average return of the momentum investment strategy, which is based on the past performance of individual firms. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Equal Weighted Value Weighted
Country 1 2 3 4 5 1-5 1 2 3 4 5 1-5
Brazil 0.350% 0.797% 1.259% 0.627% 1.161% 0.811% 0.346% 0.648% 1.354% 1.358% 1.064% 0.718%
0.539 0.080 0.008** 0.146 0.006** 0.069 0.620 0.297 0.016* 0.019* 0.085 0.325
Czech Republic -1.604% -1.057% 0.150% -0.050% -0.221% 1.382% -1.292% -0.114% 0.419% 0.380% 0.696% 1.987%
0.006** 0.085 0.694 0.911 0.539 0.015* 0.087 0.855 0.468 0.413 0.148 0.007**
Hungary -1.617% 0.257% -0.249% 0.515% 0.241% 1.858% -0.572% 0.929% 0.277% 1.253% 0.809% 1.381%
0.011* 0.588 0.621 0.292 0.660 0.002** 0.452 0.112 0.636 0.037* 0.196 0.043*
Malaysia -0.993% -0.143% -0.006% -0.086% -0.662% 0.331% -0.284% 0.319% 0.151% 0.230% -0.106% 0.178%
0.176 0.824 0.992 0.884 0.278 0.424 0.682 0.579 0.785 0.669 0.842 0.700
Mexico -0.113% 0.609% 0.805% 0.941% 0.760% 0.873% -0.176% 0.434% 0.786% 0.773% 0.818% 0.994%
0.818 0.073 0.025* 0.004** 0.020* 0.025* 0.783 0.306 0.081 0.059 0.053 0.090
Poland -1.234% -0.135% -0.407% 0.114% 0.005% 1.239% -1.114% 0.839% 0.132% 0.497% 0.125% 1.239%
0.026* 0.785 0.388 0.803 0.992 0.001** 0.089 0.117 0.806 0.360 0.797 0.036*
South Africa -1.310% -0.100% 0.727% 0.682% 0.784% 2.094% -0.339% 0.385% 1.060% 0.925% 0.738% 1.078%
0.001** 0.737 0.008** 0.015* 0.025* 0.000** 0.482 0.359 0.002** 0.017* 0.088 0.030*
Taiwan -0.463% 0.002% 0.123% -0.053% -0.376% 0.088% -0.377% 0.060% 0.269% 0.064% -0.319% 0.058%
0.397 0.997 0.771 0.899 0.409 0.801 0.497 0.900 0.513 0.878 0.453 0.880
Turkey 1.769% 2.037% 2.093% 1.787% 1.304% -0.465% 1.552% 2.114% 2.059% 2.935% 0.891% -0.660%
0.024* 0.011* 0.007** 0.014* 0.088 0.144 0.060 0.014* 0.009** 0.001** 0.396 0.460
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Table 4.7: Average monthly returns of the portfolios sorted by liquidity Note that portfolio 1 consists of firms with the highest number of zero return trading days (i.e. lowest liquidity), while portfolio 5 consists of firms with the lowest number of zero return trading days (i.e. highest liquidity). The return of the “1-5” portfolio shows the average return of the liquidity investment strategy, which is based on the liquidity of individual firms. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Equal Weighted Value Weighted
Country 1 2 3 4 5 1-5 1 2 3 4 5 1-5
Brazil 0.827% 0.952% 0.713% 0.671% 1.004% -0.177% 0.904% 0.875% 1.022% 1.060% 0.943% -0.039%
0.046* 0.028* 0.129 0.220 0.043* 0.653 0.184 0.056 0.042* 0.043* 0.122 0.960
Czech Republic -0.145% -0.358% -1.621% -0.296% -0.761% 0.616% 0.736% 0.047% -0.628% 0.436% 0.152% 0.584%
0.736 0.362 0.021* 0.496 0.166 0.257 0.073 0.881 0.353 0.298 0.782 0.314
Hungary 0.385% -0.579% -0.332% -0.674% 0.186% 0.199% 0.616% 0.146% 0.425% 0.093% 0.810% -0.194%
0.492 0.270 0.541 0.210 0.735 0.744 0.207 0.790 0.479 0.862 0.180 0.751
Malaysia -0.052% -0.140% -0.341% -0.501% -0.837% 0.785% 0.167% 0.265% -0.036% -0.108% -0.065% 0.232%
0.920 0.805 0.576 0.458 0.260 0.014* 0.683 0.538 0.939 0.847 0.908 0.463
Mexico 0.333% 0.160% 0.447% 0.063% 1.062% -0.729% 0.315% -0.316% 0.797% 0.752% 0.798% -0.483%
0.269 0.702 0.400 0.858 0.000** 0.001** 0.492 0.569 0.109 0.039* 0.014* 0.176
Poland -0.495% -0.487% -0.467% 0.150% -0.374% -0.121% 0.045% -0.176% -0.187% 0.645% 0.018% 0.026%
0.271 0.325 0.347 0.755 0.476 0.696 0.925 0.710 0.714 0.200 0.969 0.950
South Africa 0.061% 0.046% 0.032% 0.095% 0.536% -0.475% 0.735% 0.362% 0.562% 0.681% 0.884% -0.150%
0.816 0.866 0.916 0.786 0.148 0.115 0.006** 0.162 0.116 0.030* 0.020* 0.664
Taiwan -0.170% 0.137% -0.066% -0.252% -0.374% 0.204% -0.154% 0.156% 0.167% -0.017% -0.008% -0.146%
0.632 0.743 0.886 0.618 0.480 0.423 0.683 0.706 0.665 0.968 0.986 0.592
Turkey 1.723% 1.902% 1.791% 1.923% 1.665% 0.058% 1.399% 1.336% 1.782% 1.766% 2.332% -0.934%
0.008** 0.013* 0.026* 0.014* 0.043* 0.841 0.098 0.077 0.025* 0.066 0.010** 0.187
154 | Page
4.3.2. Bivariate portfolio sorts
According to Nartea et al. (2014), the MAX effect is more persistent among small stocks with high book to market ratio. Therefore, the MAX effect may be explained by these well-known cross sectional anomalies. To test such cross sectional effects in stock returns in advanced emerging markets, this study also performs bivariate sorts on MAX and size, and MAX and the book to market ratio of firms. Note that the number of available liquid stocks does not allow for the construction of five by five double-sorted portfolios and hence, the double-sorted portfolios are constructed using tercile sorts rather than quintile sorts.
Panel A and panel B of Table 4.8 show the result of bivariate sorting on
MAX and size when calculating the equal weighted and value weighted returns of portfolios respectively. In the equal weighted portfolio section, the MAX effect is persistent between all size groups in Malaysia, South Africa and Turkey.
However, the effect is more prevalent in larger stocks in Brazil and Poland with return differences of 0.968% and 1.605% per month respectively between extreme
MAX portfolios and no significant returns in smaller stocks. On the other hand, the MAX effect is dominant among smaller stocks in Taiwan with 1.329% per month significant at 1% level and no significant return among larger stocks. In the value weighted portfolio section, the MAX effect is dominant in all size groups in
Malaysia, Poland and South Africa. However, the results are more robust among larger (smaller) stocks in Brazil and Hungary (Taiwan and Turkey). The MAX effect is strongest in the medium sized group of stocks in the Czech Republic with the returns of 3.068% per month significant at 1% level. Note that the MAX effect
155 | Page is not statistically significant at any level in any size group in Mexico in both equal and value weighted portfolios. The inconsistent results from advanced emerging markets show that the MAX effect does not belong to any specifically sized group.
156 | Page
Table 4.8: Bivariate sorting results on MAX and size Note that portfolio 1 has the lowest MAX on previous month while portfolio 3 has the highest MAX on previous month. The return of the “1-3” portfolio shows the average return of the MAX investment strategy within each size group. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Panel A: Equal Weighted Country Small Medium Large
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil 1.201% 1.118% 0.856% 0.345% 0.930% 0.554% 0.306% 0.625% 1.208% 0.955% 0.240% 0.968%
0.010** 0.045* 0.139 0.546 0.043* 0.382 0.597 0.116 0.012* 0.073 0.683 0.013*
Czech Republic -0.142% -0.200% -1.763% 1.621% 0.081% -1.053% -3.029% 3.110% 0.114% 0.417% -0.062% 0.176%
0.815 0.762 0.037* 0.076 0.871 0.070 0.002** 0.002** 0.763 0.420 0.919 0.761
Hungary 0.033% 0.031% -1.633% 1.665% 0.431% 0.014% -1.479% 1.910% 1.345% 0.627% -0.470% 1.815%
0.968 0.965 0.012* 0.065 0.449 0.983 0.022* 0.003** 0.006** 0.256 0.595 0.034*
Malaysia 0.346% 0.323% -0.996% 1.342% 0.169% -0.202% -1.254% 1.423% 0.178% -0.008% -0.801% 0.979%
0.561 0.657 0.216 0.000** 0.736 0.752 0.090 0.000** 0.678 0.989 0.260 0.008**
Mexico 0.602% 0.980% 0.575% 0.027% 0.695% 0.980% -0.002% 0.697% 0.533% 1.382% 0.482% 0.051%
0.027* 0.062 0.347 0.959 0.014* 0.038* 0.997 0.111 0.079 0.001** 0.397 0.908
Poland 0.176% 0.207% -1.100% 1.276% 0.145% -0.264% -0.976% 1.121% 0.164% -0.323% -1.441% 1.605%
0.863 0.736 0.056 0.139 0.774 0.631 0.088 0.001** 0.733 0.524 0.013* 0.000**
South Africa 1.572% 1.228% -0.509% 2.080% 0.782% 0.586% -1.281% 2.063% 1.074% 0.762% -0.407% 1.481%
0.000** 0.001** 0.197 0.000** 0.009** 0.110 0.002** 0.000** 0.001** 0.027* 0.362 0.000**
Taiwan 0.620% 0.117% -0.710% 1.329% 0.007% -0.138% -0.945% 0.952% -0.140% -0.175% -0.752% 0.612%
0.166 0.830 0.249 0.000** 0.988 0.809 0.132 0.000** 0.746 0.741 0.218 0.052
Turkey 2.748% 2.340% 1.296% 1.453% 2.262% 2.117% 0.966% 1.296% 2.453% 2.241% 1.191% 1.261%
0.001** 0.003** 0.095 0.000** 0.006** 0.012* 0.242 0.000** 0.001** 0.007** 0.143 0.000**
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Panel B: Value Weighted Country Small Medium Large
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil 0.924% 1.016% 0.379% 0.544% 0.884% 0.590% 0.421% 0.464% 1.163% 0.831% -0.048% 1.211%
0.062 0.090 0.510 0.307 0.060 0.350 0.475 0.277 0.036* 0.163 0.943 0.046*
Czech Republic -0.546% -0.396% -1.729% 1.182% 0.163% -0.671% -2.905% 3.068% 0.205% 0.571% 0.004% 0.201%
0.385 0.566 0.053 0.227 0.739 0.249 0.003** 0.002** 0.629 0.296 0.995 0.728
Hungary -0.102% 0.233% -1.825% 1.723% 0.288% -0.208% -1.491% 1.779% 1.424% 0.516% -0.562% 1.986%
0.902 0.753 0.004** 0.069 0.616 0.755 0.024* 0.009** 0.006** 0.409 0.549 0.026*
Malaysia 0.321% 0.199% -1.057% 1.378% 0.136% -0.193% -1.271% 1.407% 0.218% 0.206% -0.582% 0.800%
0.581 0.779 0.184 0.000** 0.785 0.764 0.086 0.000** 0.596 0.690 0.380 0.041*
Mexico 0.725% 1.087% 0.564% 0.161% 0.797% 1.072% 0.020% 0.777% 0.083% 1.152% 0.244% -0.162%
0.009** 0.044* 0.340 0.747 0.006** 0.025* 0.971 0.052 0.831 0.011* 0.701 0.775
Poland 0.370% -0.101% -1.167% 1.536% 0.304% -0.253% -1.025% 1.329% 0.276% -0.006% -1.047% 1.323%
0.675 0.874 0.039* 0.034* 0.554 0.643 0.080 0.000** 0.578 0.991 0.091 0.008**
South Africa 1.306% 1.191% -0.886% 2.192% 0.806% 0.572% -1.353% 2.158% 1.148% 0.776% -0.100% 1.248%
0.000** 0.001** 0.026* 0.000** 0.006** 0.129 0.001** 0.000** 0.001** 0.041* 0.841 0.002**
Taiwan 0.567% 0.006% -0.832% 1.399% 0.012% -0.144% -0.885% 0.897% -0.010% -0.034% -0.643% 0.633%
0.210 0.991 0.179 0.000** 0.979 0.801 0.159 0.001** 0.980 0.944 0.268 0.059
Turkey 2.578% 1.956% 1.306% 1.272% 2.193% 2.132% 1.040% 1.153% 1.938% 2.682% 1.670% 0.268%
0.001** 0.015* 0.101 0.000** 0.008** 0.011* 0.211 0.000** 0.052 0.004** 0.060 0.752
158 | Page
Panel A and panel B of Table 4.9 show the result of bivariate sorting on
MAX and book to market ratio when calculating the equally and value weighted portfolio returns respectively. The results indicate that the MAX effect is stronger when calculating equal weighted portfolios. Panel A shows that the MAX effect is strong among all value groups in Malaysia, South Africa, Taiwan and Turkey. For instance, the return difference between extreme MAX portfolios among stocks with low, medium and high book to market ratios are 2.329%, 1.890% and
1.364% respectively in South Africa, all significant at 1% level. On the other hand, the MAX effect does not exist in any value group of stocks in Mexico. The
MAX effect is stronger in stocks with a low book to market ratio in Brazil,
Hungary and Poland. Panel B shows that the MAX effect is pervasive in all value groups of stocks in Malaysia and South Africa. However, the effect is more persistent in stocks with a low book to market ratio in Hungary and Poland. For instance, among low book to market ratio stocks, the return difference between extreme MAX portfolios is 3.313% per month in Hungary significant at a 1% level. On the other hand, the MAX effect is strong among high book to market ratio stocks with the return of 1.154% per month in Turkey, significant at 5% level. The results from the bivariate sorting based on MAX and book to market ratio indicate that first, the MAX effect is more robust when calculating equal weighted portfolio returns. Second, looking at equal weighted portfolios, the results are more persistent among low book to market ratio stocks in Brazil,
Hungary and Poland. Third, the MAX effect does not belong to any specific value group of stocks when calculating value weighted portfolio returns.
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Table 4.9: Bivariate sorting results on MAX and book to market ratio Note that portfolio 1 has the lowest MAX on previous month while portfolio 3 has the highest MAX on previous month. The return of the “1-3” portfolio shows the average return of the MAX investment strategy within each value group. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Panel A: Equal Weighted Country Low Medium High
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil 0.687% 0.409% -0.421% 1.107% 1.709% 0.744% 0.535% 1.174% 1.599% 1.243% 1.507% 0.092%
0.108 0.476 0.480 0.012* 0.000** 0.167 0.364 0.005** 0.003** 0.043* 0.015* 0.872
Czech Republic 0.210% -0.298% -1.898% 2.108% -1.016% 0.059% -0.301% -0.715% 0.263% -0.099% -1.665% 1.928%
0.670 0.614 0.034* 0.016* 0.059 0.922 0.624 0.303 0.611 0.884 0.060 0.031*
Hungary 0.089% -0.779% -2.614% 2.703% 0.227% 0.589% -1.105% 1.332% 0.049% 0.905% -0.856% 0.904%
0.878 0.233 0.029* 0.021* 0.677 0.320 0.114 0.062 0.936 0.159 0.186 0.212
Malaysia -0.302% -0.629% -1.768% 1.466% 0.510% 0.252% -0.866% 1.376% 0.704% 0.410% -0.701% 1.405%
0.504 0.304 0.020* 0.000** 0.289 0.680 0.213 0.000** 0.168 0.544 0.360 0.000**
Mexico 0.213% 0.807% -0.288% 0.501% 0.744% 1.274% 0.166% 0.579% 1.192% 1.295% 0.564% 0.629%
0.439 0.059 0.590 0.280 0.022* 0.005** 0.770 0.189 0.000** 0.013* 0.385 0.260
Poland -0.287% -0.851% -1.980% 1.692% 0.787% 0.447% -0.608% 1.395% 0.747% 0.710% -0.002% 0.750%
0.583 0.099 0.002** 0.001** 0.129 0.393 0.309 0.002** 0.174 0.201 0.997 0.108
South Africa 0.584% 0.212% -1.745% 2.329% 0.961% 0.734% -0.928% 1.890% 1.442% 1.247% 0.078% 1.364%
0.068 0.577 0.000** 0.000** 0.001** 0.038* 0.023* 0.000** 0.000** 0.000** 0.845 0.000**
Taiwan -0.883% -0.978% -1.962% 1.079% 0.252% 0.017% -0.620% 0.872% 0.935% 0.869% 0.265% 0.670%
0.040* 0.070 0.002** 0.001** 0.562 0.975 0.294 0.001** 0.068 0.155 0.686 0.007**
Turkey 1.816% 1.566% 0.402% 1.414% 2.400% 2.282% 1.037% 1.363% 3.224% 3.104% 1.809% 1.415%
0.020* 0.063 0.617 0.002** 0.004** 0.005** 0.211 0.001** 0.000** 0.001** 0.030* 0.000**
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Panel B: Value Weighted Country Low Medium High
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil 1.355% 0.610% 0.290% 1.066% 1.686% 1.028% 0.865% 0.821% 0.611% 1.327% 0.813% -0.202%
0.011** 0.326 0.694 0.138 0.002** 0.084 0.165 0.084 0.308 0.077 0.257 0.763
Czech Republic 0.255% 0.173% -1.523% 1.778% -0.542% 0.664% 0.114% -0.657% 0.264% -0.179% -0.494% 0.758%
0.643 0.779 0.111 0.057 0.270 0.271 0.868 0.357 0.620 0.803 0.568 0.393
Hungary 0.424% -0.232% -2.889% 3.313% 0.559% 0.802% -0.840% 1.399% 0.244% 1.396% -0.628% 0.872%
0.440 0.728 0.009** 0.002** 0.315 0.200 0.256 0.061 0.686 0.052 0.378 0.271
Malaysia 0.123% 0.088% -0.984% 1.108% 0.606% 0.438% -0.699% 1.305% 0.744% 0.538% -0.425% 1.169%
0.767 0.858 0.154 0.008** 0.169 0.439 0.270 0.000** 0.136 0.429 0.570 0.001**
Mexico 0.548% 1.118% -0.042% 0.590% 0.671% 1.356% 0.970% -0.299% 1.157% 1.231% 0.104% 1.053%
0.174 0.014* 0.942 0.317 0.107 0.005** 0.109 0.562 0.001** 0.036* 0.902 0.174
Poland 0.148% 0.002% -1.491% 1.638% 1.180% 0.286% -0.290% 1.470% 1.098% 0.906% -0.254% 1.352%
0.785 0.997 0.031* 0.009** 0.030* 0.645 0.689 0.016* 0.060 0.158 0.751 0.075
South Africa 0.544% 0.638% -0.601% 1.146% 1.337% 0.753% -0.118% 1.455% 1.577% 1.110% 0.082% 1.495%
0.127 0.134 0.271 0.012* 0.000** 0.054 0.803 0.000** 0.000** 0.015* 0.875 0.001**
Taiwan -0.437% -0.023% -1.125% 0.688% 0.504% 0.321% -0.301% 0.805% 0.798% 0.694% 0.411% 0.387%
0.317 0.963 0.065 0.077 0.219 0.548 0.601 0.018* 0.125 0.253 0.526 0.179
Turkey 2.187% 2.433% 1.263% 0.924% 2.710% 2.507% 1.479% 1.231% 3.442% 3.179% 2.288% 1.154%
0.017* 0.006** 0.151 0.207 0.001** 0.007** 0.121 0.038* 0.000** 0.001** 0.014* 0.023*
161 | Page
4.3.3. Time series regression analysis
To examine the risk-adjusted returns of MAX-sorted portfolios, two different asset pricing models have been applied to estimate expected returns: the Fama and
French (1993) three-factor model, and an augmented model that adds the constructed momentum (MOM) and liquidity (LIQ) factors to the three-factor model. The regression equations are as follows:
= + + ( ) + ( ) + (4.1)
𝑟𝑟𝑝𝑝𝑝𝑝𝑝𝑝 − 𝑟𝑟𝑓𝑓𝑓𝑓 𝛼𝛼𝑖𝑖𝑖𝑖 𝛽𝛽1𝑖𝑖�𝑟𝑟𝑚𝑚𝑚𝑚 − 𝑟𝑟𝑓𝑓𝑓𝑓� 𝛽𝛽2𝑖𝑖 𝑆𝑆𝑆𝑆𝑆𝑆𝑡𝑡 𝛽𝛽3𝑖𝑖 𝐻𝐻𝐻𝐻𝐻𝐻𝑡𝑡 𝜀𝜀𝑖𝑖𝑖𝑖 = + + ( ) + ( ) + ( ) +
𝑝𝑝𝑝𝑝𝑝𝑝 𝑓𝑓𝑓𝑓 𝑖𝑖𝑖𝑖 1𝑖𝑖 𝑚𝑚𝑚𝑚 𝑓𝑓𝑓𝑓 2𝑖𝑖 𝑡𝑡 3𝑖𝑖 𝑡𝑡 4𝑖𝑖 𝑡𝑡 𝑟𝑟 (− 𝑟𝑟 ) +𝛼𝛼 𝛽𝛽 � 𝑟𝑟 − 𝑟𝑟 � 𝛽𝛽 𝑆𝑆𝑆𝑆𝑆𝑆 𝛽𝛽 𝐻𝐻𝐻𝐻𝐻𝐻 𝛽𝛽 𝑀𝑀𝑀𝑀𝑀𝑀(4.2)
𝛽𝛽5𝑖𝑖 𝐿𝐿𝐿𝐿𝐿𝐿𝑡𝑡 𝜀𝜀𝑖𝑖𝑖𝑖 In the above equations, is the return of the portfolios sorted based on
𝑝𝑝𝑝𝑝𝑝𝑝 MAX effect in month t and 𝑟𝑟 is the three-month Treasury Bill Rate of the
𝑓𝑓𝑓𝑓 country under study in month t𝑟𝑟. is the return of the constructed market index
𝑚𝑚𝑚𝑚 in month t. is the difference𝑟𝑟 between returns of the extreme portfolios
𝑡𝑡 sorted based 𝑆𝑆𝑆𝑆𝑆𝑆on size in month t. Similarly, , and are the
𝑡𝑡 𝑡𝑡 𝑡𝑡 difference between returns of the extreme portfolios𝐻𝐻𝐻𝐻𝐻𝐻 𝑀𝑀sorted𝑀𝑀𝑀𝑀 based𝐿𝐿 𝐿𝐿𝐿𝐿on book to market ratio, momentum and liquidity respectively for month t and is the error
𝑖𝑖𝑖𝑖 term. This study performs the above regressions for each univariate/bivariate𝜀𝜀 sorted portfolio based on MAX, and estimates the alpha. Any difference in the alphas estimated from sorted portfolios presents the part of the returns related to the MAX effect that could not be explained by the risk factors that are embedded within the above models.
162 | Page
Panel A and panel B of Table 4.10 shows the alphas of the time series regression results derived from equations (4.1) and (4.2) respectively. Table 4.10 shows that the results are similar before and after including the momentum and liquidity factors in the regression analysis. For instance, the differences in alphas from extreme MAX portfolios are significant in both equal and value weighted portfolios in the Czech Republic, Hungary, Poland and South Africa and not significant in both equal and value weighted portfolios in Brazil and Mexico, before and after including momentum and liquidity. The return variation in extreme MAX portfolios is highest in value weighted portfolios in Hungary, which is 2.85% and 3.19% per month before and after, including momentum and liquidity. These high and significant alphas show the part of return variations that cannot be explained by existing risk factors; market beta, size, book to market ratio, momentum and liquidity. Such great return variations may have been caused by another inherent risk factor or by mispricing.
163 | Page
Table 4.10: Alpha results from time series regressions on portfolios sorted on MAX Note that portfolio 1 has the lowest MAX on previous month while portfolio 5 has the highest MAX on previous month. The return of the “1-5” portfolio shows the average risk adjusted return of the MAX investment strategy. Panel A shows the results when we include the Fama and French three factor model in the regression and panel B shows the results when we use the Fama and French three factor model plus the constructed momentum and liquidity factors. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Panel A: Market Beta, SMB and HML Country Equal Weighted Value Weighted
MAX1 MAX2 MAX3 MAX4 MAX5 1-5 MAX1 MAX2 MAX3 MAX4 MAX5 1-5
Brazil 0.09% 0.24% 0.11% 0.26% -0.54% 0.63% 0.09% -0.08% 0.00% 0.11% -0.83% 0.92%
0.662 0.205 0.587 0.168 0.036* 0.094 0.774 0.770 0.995 0.804 0.111 0.140
Czech Republic 0.62% 0.46% 0.74% 0.21% -1.46% 2.08% 0.45% 0.37% 0.49% 0.23% -1.66% 2.11%
0.030* 0.197 0.038* 0.600 0.001** 0.000** 0.150 0.343 0.226 0.687 0.009** 0.004**
Hungary 0.41% 0.65% 0.33% 0.17% -1.54% 1.96% 0.29% 0.21% 0.15% -0.45% -2.56% 2.85%
0.228 0.053 0.320 0.672 0.000** 0.000** 0.509 0.544 0.687 0.371 0.001** 0.001**
Malaysia 0.49% 0.49% 0.25% -0.02% -1.17% 1.65% 0.22% 0.07% -0.08% -0.22% -1.21% 1.43%
0.000** 0.000** 0.000** 0.772 0.000** 0.000** 0.072 0.548 0.574 0.217 0.000** 0.000**
Mexico -0.29% 0.40% 0.28% 0.46% -0.58% 0.29% -0.43% 0.22% 0.49% 0.43% -0.63% 0.20%
0.071 0.010** 0.030* 0.006** 0.006** 0.357 0.242 0.363 0.033* 0.104 0.098 0.723
Poland 0.41% 0.37% 0.51% -0.11% -0.79% 1.19% 0.20% 0.36% -0.12% -0.63% -1.25% 1.45%
0.022* 0.043* 0.004** 0.555 0.000** 0.000** 0.422 0.138 0.668 0.035* 0.019* 0.020*
South Africa 0.52% 0.56% 0.26% -0.06% -1.21% 1.73% 0.63% 0.17% -0.15% -0.48% -1.96% 2.58%
0.000** 0.000** 0.007** 0.621 0.000** 0.000** 0.001** 0.222 0.295 0.040* 0.000** 0.000**
Taiwan 0.20% 0.31% 0.16% -0.02% -0.55% 0.75% 0.05% -0.05% 0.07% -0.26% -0.36% 0.41%
0.093 0.000** 0.038* 0.847 0.000** 0.001** 0.787 0.711 0.621 0.144 0.091 0.211
Turkey 0.41% 0.81% 0.28% -0.14% -1.13% 1.54% -0.80% 0.55% 0.31% -0.25% -1.48% 0.68%
0.004** 0.000** 0.036* 0.355 0.000** 0.000** 0.138 0.327 0.567 0.655 0.013* 0.343
164 | Page
Panel B: Market Beta, SMB, HML, Momentum and Liquidity Country Equal Weighted Value Weighted
MAX1 MAX2 MAX3 MAX4 MAX5 1-5 MAX1 MAX2 MAX3 MAX4 MAX5 1-5
Brazil 0.04% 0.29% -0.05% 0.31% -0.42% 0.46% 0.08% -0.03% 0.05% 0.22% -0.81% 0.90%
0.852 0.128 0.771 0.097 0.085 0.210 0.783 0.912 0.883 0.591 0.132 0.160
Czech Republic 0.55% 0.47% 0.68% 0.28% -1.38% 1.93% 0.47% 0.59% 0.67% 0.23% -1.76% 2.23%
0.058 0.220 0.070 0.494 0.002** 0.001** 0.138 0.141 0.116 0.689 0.009** 0.004**
Hungary 0.47% 0.44% 0.50% 0.13% -1.54% 2.00% 0.44% 0.06% 0.28% -0.42% -2.75% 3.19%
0.187 0.200 0.156 0.756 0.001** 0.000** 0.324 0.863 0.474 0.422 0.000** 0.000**
Malaysia 0.36% 0.51% 0.31% 0.04% -1.15% 1.52% 0.14% 0.08% -0.04% -0.15% -1.13% 1.27%
0.000** 0.000** 0.000** 0.605 0.000** 0.000** 0.223 0.497 0.773 0.409 0.000** 0.000**
Mexico -0.34% 0.38% 0.29% 0.32% -0.37% 0.04% -0.29% 0.03% 0.30% 0.29% -0.49% 0.20%
0.042* 0.018* 0.036* 0.059 0.070 0.909 0.444 0.912 0.179 0.276 0.203 0.726
Poland 0.28% 0.44% 0.61% -0.08% -0.85% 1.13% 0.07% 0.48% 0.07% -0.59% -1.37% 1.44%
0.128 0.006** 0.001** 0.662 0.000** 0.001** 0.780 0.036* 0.804 0.055 0.011* 0.024*
South Africa 0.51% 0.57% 0.28% -0.04% -1.27% 1.79% 0.57% 0.22% -0.17% -0.51% -1.98% 2.55%
0.000** 0.000** 0.004** 0.774 0.000** 0.000** 0.008** 0.146 0.288 0.047* 0.000** 0.000**
Taiwan 0.25% 0.33% 0.16% -0.05% -0.57% 0.82% 0.08% -0.03% 0.05% -0.30% -0.42% 0.50%
0.032* 0.000** 0.036* 0.579 0.000** 0.000** 0.668 0.811 0.735 0.104 0.055 0.137
Turkey 0.49% 0.82% 0.31% -0.21% -1.26% 1.74% -0.66% 0.66% 0.09% -0.82% -1.79% 1.13%
0.001** 0.000** 0.019* 0.161 0.000** 0.000** 0.223 0.213 0.850 0.111 0.002** 0.113
165 | Page
Tables 4.11 and 4.12 show the alphas of the time series regression results when performing regression equations (4.1) and (4.2) respectively on bivariate- sorted portfolios based on MAX and size. The results are similar for equal and value weighted portfolios before and after including the momentum and liquidity factors. The alpha spread between extreme MAX portfolios are highest among medium size stocks in the Czech Republic with 2.60% and 3.11% per month significant at 1% level for equal and value weighted portfolios respectively. After including the momentum and liquidity factor, these numbers become 2.40% and
2.91% per month significant at 1% level for equal and value weighted portfolios respectively. On the other hand, the MAX effect does not exist in Mexico and
Brazil. Note that the MAX effect exists in Brazil in portfolio sort analysis but not in time series analysis. This indicates that the MAX effect in the sorting portfolios can be explained by existing risk factors in the time series regression equations.
166 | Page
Table 4.11: Alpha results from time series regression on double sorted Size and MAX portfolios (3 Factor Model) Note that portfolio 1 has the lowest MAX on previous month while portfolio 3 has the highest MAX on previous month. The return of the “1-3” portfolio shows the average risk adjusted return of the MAX investment strategy within each size group. Note that the Fama and French three-factor model has been used in the regression. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Panel A: Equal Weighted Countries Small Medium Large
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil 0.08% 0.16% -0.10% 0.17% 0.02% -0.13% -0.47% 0.48% 0.30% 0.11% -0.56% 0.85%
0.824 0.613 0.794 0.762 0.949 0.732 0.136 0.218 0.240 0.671 0.123 0.031*
Czech Republic 0.22% 0.33% -0.71% 0.93% 0.21% -0.76% -2.39% 2.60% 0.12% 0.64% 0.22% -0.11%
0.687 0.558 0.249 0.290 0.640 0.096 0.001** 0.003** 0.674 0.080 0.572 0.843
Hungary -0.05% 0.34% -0.23% 0.19% 0.20% -0.01% -1.83% 2.03% 0.99% 0.29% -0.69% 1.67%
0.953 0.600 0.553 0.831 0.659 0.984 0.002** 0.003** 0.011* 0.459 0.400 0.059
Malaysia 0.87% 0.96% -0.40% 1.27% 0.40% -0.01% -1.06% 1.47% 0.30% 0.02% -0.92% 1.21%
0.000** 0.000** 0.000** 0.000** 0.000** 0.889 0.000** 0.000** 0.001** 0.857 0.000** 0.000**
Mexico -0.23% 0.40% 0.10% -0.33% -0.17% 0.40% -0.74% 0.56% -0.25% 0.56% -0.16% -0.09%
0.285 0.118 0.655 0.374 0.344 0.025* 0.002** 0.099 0.251 0.000** 0.444 0.790
Poland 0.91% 0.81% -0.34% 1.25% 0.26% 0.07% -0.47% 0.73% 0.45% 0.06% -1.10% 1.55%
0.276 0.010** 0.139 0.172 0.301 0.777 0.079 0.035* 0.030* 0.756 0.002** 0.000**
South Africa 1.18% 1.01% -0.62% 1.80% 0.31% 0.30% -1.45% 1.76% 0.48% 0.18% -0.98% 1.46%
0.000** 0.000** 0.000** 0.000** 0.049* 0.028* 0.000** 0.000** 0.000** 0.085 0.002** 0.000**
Taiwan 0.75% 0.28% -0.44% 1.19% 0.20% 0.11% -0.50% 0.70% -0.20% 0.08% -0.31% 0.11%
0.000** 0.009** 0.002** 0.000** 0.117 0.289 0.000** 0.001** 0.176 0.360 0.059 0.698
Turkey 0.87% 0.38% -0.67% 1.53% 0.36% 0.29% -0.99% 1.35% 0.53% 0.36% -0.60% 1.13%
0.000** 0.055 0.001** 0.000** 0.051 0.108 0.000** 0.000** 0.001** 0.016* 0.008** 0.000**
167 | Page
Panel B: Value Weighted Countries Small Medium Large
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil -0.24% -0.07% -0.70% 0.46% -0.19% -0.37% -0.53% 0.34% 0.12% -0.11% -1.07% 1.19%
0.525 0.862 0.055 0.376 0.535 0.377 0.133 0.418 0.765 0.778 0.054 0.053
Czech Republic -0.69% -0.16% -1.28% 0.59% 0.00% -0.83% -3.11% 3.11% -0.02% 0.42% -0.10% 0.08%
0.232 0.774 0.087 0.531 0.992 0.090 0.000** 0.001** 0.934 0.253 0.805 0.890
Hungary -0.02% 0.50% -1.84% 1.82% -0.36% -0.66% -2.11% 1.75% 0.70% -0.37% -1.24% 1.94%
0.981 0.439 0.001** 0.066 0.412 0.213 0.000** 0.013* 0.036* 0.361 0.140 0.035*
Malaysia 0.62% 0.63% -0.56% 1.19% 0.18% -0.11% -1.18% 1.37% 0.13% 0.01% -0.82% 0.95%
0.000** 0.000** 0.000** 0.000** 0.178 0.417 0.000** 0.000** 0.110 0.969 0.000** 0.000**
Mexico -0.16% 0.50% 0.04% -0.21% -0.03% 0.47% -0.52% 0.49% -0.72% 0.56% -0.17% -0.55%
0.468 0.107 0.883 0.579 0.875 0.049* 0.066 0.123 0.018* 0.002** 0.537 0.234
Poland 1.11% 0.27% -0.72% 1.83% 0.36% -0.08% -0.78% 1.14% 0.09% -0.07% -1.01% 1.10%
0.088 0.511 0.003** 0.013* 0.254 0.808 0.027* 0.002** 0.683 0.759 0.006** 0.028*
South Africa 1.14% 1.05% -0.89% 2.02% 0.43% 0.27% -1.66% 2.09% 0.32% -0.06% -1.00% 1.32%
0.000** 0.000** 0.000** 0.000** 0.027* 0.179 0.000** 0.000** 0.043* 0.590 0.003** 0.002**
Taiwan 0.73% 0.27% -0.51% 1.24% 0.10% -0.03% -0.66% 0.76% -0.03% 0.06% -0.44% 0.41%
0.000** 0.013* 0.000** 0.000** 0.478 0.843 0.000** 0.000** 0.836 0.638 0.033* 0.173
Turkey 0.44% -0.13% -0.87% 1.31% -0.07% -0.02% -1.33% 1.25% -0.51% 0.33% -0.72% 0.20%
0.124 0.636 0.001** 0.000** 0.823 0.965 0.001** 0.000** 0.326 0.549 0.217 0.815
168 | Page
Table 4.12: Alpha results from time series regression on double sorted Size and MAX portfolios (5 Factor Model) Note that portfolio 1 has the lowest MAX on previous month while portfolio 3 has the highest MAX on previous month. The return of the “1-3” portfolio shows the average risk adjusted return of the MAX investment strategy within each size group. Note that five factors have been included in the regression, which are Fama and French three-factor model plus the momentum and liquidity factor. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Panel A: Equal Weighted Countries Small Medium Large
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil 0.05% 0.46% 0.12% -0.07% 0.07% -0.31% -0.43% 0.50% 0.15% -0.03% -0.62% 0.78%
0.886 0.141 0.729 0.905 0.778 0.410 0.180 0.216 0.522 0.883 0.089 0.055
Czech Republic 0.05% 0.26% -0.56% 0.61% 0.09% -0.78% -2.30% 2.40% 0.02% 0.22% 0.22% -0.19%
0.932 0.656 0.356 0.491 0.837 0.103 0.001** 0.009** 0.934 0.599 0.599 0.731
Hungary 0.52% 0.54% -0.20% 0.72% 0.14% 0.18% -1.84% 1.98% 0.91% -0.17% -0.17% 1.08%
0.558 0.428 0.631 0.434 0.762 0.743 0.003** 0.005** 0.013* 0.841 0.841 0.237
Malaysia 0.83% 1.03% -0.32% 1.15% 0.33% 0.03% -1.01% 1.34% 0.17% -0.83% -0.83% 1.00%
0.000** 0.000** 0.001** 0.000** 0.002** 0.789 0.000** 0.000** 0.054 0.000** 0.000** 0.000**
Mexico -0.17% 0.49% 0.15% -0.32% -0.18% 0.28% -0.67% 0.49% -0.42% -0.25% -0.25% -0.17%
0.426 0.083 0.515 0.386 0.333 0.135 0.008** 0.167 0.066 0.242 0.242 0.623
Poland 0.79% 1.16% -0.28% 1.07% 0.06% -0.04% -0.43% 0.48% 0.43% -1.29% -1.29% 1.73%
0.385 0.000** 0.217 0.278 0.793 0.862 0.108 0.156 0.052 0.000** 0.000** 0.000**
South Africa 1.20% 1.04% -0.63% 1.83% 0.46% 0.35% -1.34% 1.80% 0.42% -1.25% -1.25% 1.66%
0.000** 0.000** 0.000** 0.000** 0.004** 0.016* 0.000** 0.000** 0.000** 0.000** 0.000** 0.000**
Taiwan 0.78% 0.32% -0.40% 1.18% 0.24% 0.10% -0.52% 0.76% -0.16% -0.44% -0.44% 0.28%
0.000** 0.004** 0.005** 0.000** 0.066 0.380 0.000** 0.000** 0.298 0.008** 0.008** 0.324
Turkey 0.72% 0.37% -0.79% 1.51% 0.38% 0.40% -1.16% 1.54% 0.63% -0.64% -0.64% 1.27%
0.001** 0.070 0.000** 0.000** 0.041* 0.033* 0.000** 0.000** 0.000** 0.004** 0.004** 0.000**
169 | Page
Panel B: Value Weighted Countries Small Medium Large
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil -0.18% 0.21% -0.67% 0.48% -0.15% -0.32% -0.50% 0.35% 0.04% -0.15% -0.98% 1.02%
0.640 0.609 0.071 0.365 0.621 0.436 0.166 0.418 0.929 0.687 0.064 0.094
Czech Republic -0.67% -0.18% -1.14% 0.47% -0.05% -0.73% -2.96% 2.91% -0.06% -0.18% -0.18% 0.12%
0.276 0.767 0.145 0.637 0.913 0.154 0.001** 0.004** 0.836 0.678 0.678 0.845
Hungary 0.17% 0.64% -1.51% 1.69% -0.27% -0.39% -2.28% 2.01% 0.69% -1.19% -1.19% 1.88%
0.836 0.339 0.007** 0.103 0.554 0.476 0.000** 0.005** 0.018* 0.175 0.175 0.045*
Malaysia 0.64% 0.66% -0.51% 1.14% 0.16% -0.06% -1.15% 1.31% 0.09% -0.75% -0.75% 0.84%
0.000** 0.000** 0.000** 0.000** 0.222 0.661 0.000** 0.000** 0.278 0.001** 0.001** 0.001**
Mexico -0.07% 0.65% 0.07% -0.14% -0.02% 0.38% -0.52% 0.50% -0.83% -0.20% -0.20% -0.64%
0.763 0.040* 0.810 0.708 0.930 0.112 0.068 0.122 0.009** 0.474 0.474 0.175
Poland 1.21% 0.79% -0.62% 1.84% 0.18% -0.05% -0.74% 0.92% 0.06% -1.06% -1.06% 1.12%
0.074 0.006** 0.010** 0.016* 0.521 0.866 0.036* 0.009** 0.809 0.006** 0.006** 0.031*
South Africa 0.97% 0.90% -0.85% 1.82% 0.30% 0.11% -1.70% 1.99% 0.30% -0.87% -0.87% 1.17%
0.001** 0.000** 0.000** 0.000** 0.129 0.620 0.000** 0.000** 0.084 0.020* 0.020* 0.010**
Taiwan 0.75% 0.27% -0.54% 1.29% 0.13% -0.04% -0.69% 0.81% -0.01% -0.52% -0.52% 0.52%
0.000** 0.013* 0.000** 0.000** 0.401 0.800 0.000** 0.000** 0.956 0.013* 0.013* 0.095
Turkey 0.18% -0.20% -0.98% 1.16% -0.08% 0.05% -1.37% 1.29% -0.22% -1.15% -1.15% 0.93%
0.523 0.466 0.000** 0.001** 0.802 0.880 0.000** 0.000** 0.645 0.026* 0.026* 0.217
170 | Page
Tables 4.13 and 4.14 show the alphas of the time series regression results when performing regression equations (4.1) and (4.2) respectively on bivariate- sorted portfolios based on MAX and book to market ratio. The results are similar before and after including the momentum and liquidity factors, indicating that these two factors have minimal explanatory power over the variation in returns of bivariate-sorted portfolios. The alpha spreads between extreme MAX portfolios are high and significant across all value group stocks in Malaysia and South
Africa. On the other hand, the MAX effect does not exist in the Czech Republic and Mexico. The alpha spread is highest among stocks with low book to market ratio in Hungary when calculating the value weighted returns, which is 2.71%
(3.14%) per month before (after) including momentum and liquidity factors.
171 | Page
Table 4.13: Alpha results from time series regression on double sorted BV/MV and MAX portfolios (3 Factor Model) Note that portfolio 1 has the lowest MAX on previous month while portfolio 3 has the highest MAX on previous month. The return of the “1-3” portfolio shows the average risk adjusted return of the MAX investment strategy within each value group. Note that the Fama and French three-factor model has been used in the regression. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively. Panel A: Equal Weighted Country Low Medium High
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil -0.29% -0.19% -0.96% 0.67% 0.74% -0.07% -0.38% 1.12% 0.35% 0.33% 0.50% -0.15%
0.257 0.527 0.004** 0.112 0.001** 0.775 0.258 0.008** 0.359 0.262 0.151 0.791
Czech Republic 0.34% 0.15% -0.91% 1.25% -0.85% 0.32% -0.09% -0.77% 0.36% 0.22% -0.90% 1.25%
0.422 0.735 0.102 0.100 0.055 0.544 0.879 0.270 0.439 0.695 0.101 0.108
Hungary 0.02% -0.68% 0.23% -0.21% -0.06% 0.32% -1.23% 1.17% -0.17% 0.69% -0.71% 0.54%
0.967 0.213 0.794 0.834 0.897 0.491 0.053 0.120 0.762 0.224 0.139 0.446
Malaysia 0.04% -0.23% -1.31% 1.35% 0.64% 0.42% -0.73% 1.37% 0.70% 0.38% -0.74% 1.44%
0.675 0.027* 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.001** 0.000** 0.000**
Mexico -0.43% 0.28% -0.53% 0.10% 0.02% 0.51% -0.22% 0.24% 0.19% 0.37% -0.46% 0.65%
0.035* 0.102 0.053 0.795 0.932 0.005** 0.399 0.510 0.437 0.155 0.067 0.114
Poland 0.34% -0.06% -0.79% 1.13% 1.12% 0.80% -0.32% 1.44% 0.75% 0.60% 0.00% 0.76%
0.165 0.820 0.055 0.030* 0.000** 0.002** 0.411 0.003** 0.013* 0.015* 0.991 0.122
South Africa 0.22% 0.16% -1.38% 1.60% 0.36% 0.24% -1.14% 1.50% 0.64% 0.37% -0.84% 1.48%
0.074 0.267 0.000** 0.000** 0.004** 0.090 0.000** 0.000** 0.002** 0.050* 0.000** 0.000**
Taiwan -0.29% 0.15% -0.75% 0.46% 0.17% 0.13% -0.14% 0.31% 0.36% 0.02% -0.44% 0.80%
0.081 0.196 0.000** 0.090 0.186 0.265 0.377 0.176 0.011* 0.889 0.004** 0.000**
Turkey 0.27% -0.13% -0.79% 1.06% 0.61% 0.59% -0.77% 1.38% 1.24% 0.85% -0.57% 1.81%
0.394 0.660 0.015* 0.038* 0.009** 0.010** 0.038* 0.004** 0.000** 0.003** 0.063 0.000**
172 | Page
Panel B: Value Weighted Country Low Medium High
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil 0.27% -0.39% -0.71% 0.99% 0.69% 0.12% -0.09% 0.78% -0.52% 0.45% -0.19% -0.32%
0.510 0.408 0.263 0.173 0.045* 0.740 0.829 0.105 0.268 0.387 0.710 0.627
Czech Republic 0.07% 0.21% -1.24% 1.31% -0.78% 0.59% 0.10% -0.88% -0.07% -0.40% -0.69% 0.62%
0.883 0.647 0.100 0.141 0.058 0.275 0.865 0.217 0.888 0.502 0.280 0.456
Hungary -0.19% -0.61% -2.90% 2.71% -0.14% 0.17% -1.58% 1.44% -0.41% 0.95% -0.93% 0.52%
0.652 0.287 0.004** 0.015* 0.757 0.714 0.018* 0.067 0.463 0.117 0.103 0.524
Malaysia 0.06% -0.10% -1.03% 1.09% 0.51% 0.29% -0.91% 1.41% 0.59% 0.39% -0.62% 1.20%
0.591 0.593 0.000** 0.000** 0.001** 0.104 0.000** 0.000** 0.000** 0.014* 0.000** 0.000**
Mexico -0.29% 0.50% -0.60% 0.31% -0.09% 0.74% 0.46% -0.55% 0.31% 0.68% -0.18% 0.49%
0.394 0.026* 0.083 0.569 0.771 0.010** 0.199 0.241 0.291 0.069 0.724 0.419
Poland 0.03% 0.00% -1.43% 1.46% 0.91% 0.22% -0.27% 1.18% 1.07% 0.85% 0.09% 0.98%
0.908 0.998 0.006** 0.022* 0.006** 0.545 0.604 0.054 0.006** 0.059 0.881 0.195
South Africa -0.15% -0.02% -1.22% 1.07% 0.74% 0.03% -0.74% 1.48% 0.54% 0.11% -0.97% 1.50%
0.401 0.920 0.003** 0.023* 0.000** 0.869 0.050* 0.001** 0.040* 0.731 0.009** 0.002**
Taiwan -0.28% 0.33% -0.61% 0.33% 0.16% 0.13% -0.46% 0.63% 0.39% 0.26% 0.00% 0.39%
0.198 0.070 0.019* 0.361 0.358 0.445 0.048* 0.043* 0.027* 0.088 0.981 0.136
Turkey 0.15% 0.12% -0.73% 0.88% 0.18% 0.09% -0.99% 1.17% 1.14% 0.89% -0.14% 1.28%
0.809 0.840 0.225 0.260 0.717 0.888 0.116 0.063 0.016* 0.109 0.801 0.017*
173 | Page
Table 4.14: Alpha results from time series regression on double sorted BV/MV and MAX portfolios (5 Factor Model) Note that portfolio 1 has the lowest MAX on previous month while portfolio 3 has the highest MAX on previous month. The return of the “1-3” portfolio shows the average risk adjusted return of the MAX investment strategy within each value group. Note that five factors have been included in the regression, which are the Fama and French three-factor model plus the momentum and liquidity factor. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
Panel A: Equal Weighted Country Low Medium High
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil -0.26% -0.44% -0.99% 0.72% 0.73% 0.03% -0.63% 1.36% 0.22% 0.41% 0.76% -0.53%
0.296 0.099 0.002** 0.093 0.002** 0.902 0.064 0.002** 0.563 0.166 0.026* 0.346
Czech Republic 0.34% 0.28% -1.02% 1.36% -1.06% 0.34% -0.86% -0.20% 0.44% 0.22% -0.79% 1.23%
0.443 0.541 0.076 0.086 0.010** 0.539 0.076 0.740 0.352 0.714 0.163 0.128
Hungary 0.30% -0.79% -0.91% 1.21% -0.05% 0.60% -0.83% 0.78% -0.03% 0.69% -0.81% 0.78%
0.497 0.172 0.323 0.236 0.914 0.213 0.219 0.323 0.955 0.248 0.103 0.289
Malaysia -0.09% -0.16% -1.32% 1.22% 0.55% 0.39% -0.67% 1.22% 0.67% 0.56% -0.60% 1.27%
0.294 0.142 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000**
Mexico -0.35% 0.30% -0.53% 0.18% -0.14% 0.54% -0.17% 0.03% 0.15% 0.15% -0.31% 0.47%
0.097 0.075 0.070 0.673 0.556 0.004** 0.521 0.933 0.532 0.593 0.194 0.244
Poland 0.01% -0.02% -1.16% 1.17% 0.90% 0.58% -0.49% 1.39% 0.62% 0.77% 0.10% 0.52%
0.960 0.922 0.006** 0.027* 0.001** 0.022* 0.227 0.006** 0.052 0.002** 0.767 0.316
South Africa 0.19% 0.12% -1.39% 1.58% 0.32% 0.28% -1.17% 1.50% 0.77% 0.63% -0.96% 1.72%
0.135 0.448 0.000** 0.000** 0.014* 0.065 0.000** 0.000** 0.000** 0.001** 0.000** 0.000**
Taiwan -0.35% 0.14% -0.77% 0.43% 0.27% 0.15% -0.19% 0.45% 0.45% 0.11% -0.43% 0.88%
0.041* 0.215 0.000** 0.121 0.044 0.186 0.248 0.053 0.002** 0.387 0.006** 0.000**
Turkey 0.41% 0.09% -1.03% 1.44% 0.70% 0.40% -0.66% 1.36% 1.13% 0.92% -0.57% 1.71%
0.197 0.713 0.001** 0.004** 0.003** 0.058 0.059 0.003** 0.000** 0.002** 0.056 0.000**
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Panel B: Value Weighted Country Low Medium High
MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3 MAX1 MAX2 MAX3 1-3
Brazil 0.21% -0.34% -0.64% 0.85% 0.63% 0.19% -0.28% 0.91% -0.61% 0.29% -0.08% -0.53%
0.599 0.421 0.276 0.236 0.069 0.573 0.501 0.064 0.200 0.581 0.877 0.428
Czech Republic 0.03% 0.45% -1.13% 1.16% -0.86% 0.88% -0.62% -0.23% 0.14% -0.48% -0.55% 0.69%
0.952 0.340 0.156 0.222 0.048* 0.114 0.218 0.721 0.783 0.445 0.416 0.436
Hungary 0.08% -0.68% -3.06% 3.14% -0.04% 0.40% -1.62% 1.58% -0.43% 1.09% -0.95% 0.52%
0.829 0.254 0.004** 0.005** 0.934 0.375 0.022* 0.050* 0.445 0.083 0.100 0.522
Malaysia 0.02% -0.02% -0.91% 0.93% 0.50% 0.30% -0.89% 1.38% 0.56% 0.45% -0.53% 1.09%
0.878 0.895 0.001** 0.002** 0.002** 0.102 0.000** 0.000** 0.001** 0.002** 0.002** 0.000**
Mexico -0.29% 0.37% -0.89% 0.60% -0.21% 0.67% 0.35% -0.56% 0.37% 0.67% 0.20% 0.17%
0.401 0.097 0.009** 0.287 0.520 0.023* 0.333 0.235 0.225 0.084 0.686 0.771
Poland -0.14% 0.21% -1.59% 1.45% 0.82% 0.25% -0.42% 1.23% 1.04% 1.00% 0.26% 0.79%
0.615 0.472 0.003** 0.025* 0.015* 0.495 0.409 0.040* 0.010** 0.031* 0.662 0.318
South Africa -0.22% -0.14% -1.42% 1.21% 0.72% -0.01% -0.80% 1.52% 0.40% 0.53% -0.81% 1.20%
0.240 0.453 0.002** 0.018* 0.001** 0.962 0.052 0.001** 0.157 0.104 0.045* 0.020*
Taiwan -0.31% 0.29% -0.68% 0.37% 0.21% 0.15% -0.55% 0.76% 0.46% 0.27% -0.03% 0.50%
0.158 0.100 0.011* 0.319 0.246 0.417 0.024* 0.017* 0.010** 0.078 0.861 0.069
Turkey 0.17% -0.31% -1.07% 1.24% 0.12% -0.10% -1.33% 1.46% 0.77% 0.59% -0.37% 1.14%
0.776 0.565 0.056 0.089 0.807 0.876 0.035* 0.019* 0.098 0.256 0.495 0.039*
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4.3.4. Panel regression analysis
To perform the panel regression analysis, this study uses four alternative models for each advanced emerging country separately. The regression equations are as follows:
= + + (4.3)
𝑟𝑟𝑖𝑖𝑖𝑖 𝛼𝛼𝑖𝑖𝑖𝑖 𝛽𝛽𝑖𝑖𝑖𝑖𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖−1 𝜀𝜀𝑖𝑖𝑖𝑖 = + + + + (4.4)
𝑟𝑟𝑖𝑖𝑖𝑖 𝛼𝛼𝑖𝑖𝑖𝑖 𝛽𝛽1𝑖𝑖𝑖𝑖𝑀𝑀𝑀𝑀𝑀𝑀𝑖𝑖𝑖𝑖−1 𝛽𝛽2𝑖𝑖𝑖𝑖𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑖𝑖𝑖𝑖 𝛽𝛽3𝑖𝑖𝑖𝑖𝐵𝐵𝐵𝐵𝑖𝑖𝑖𝑖 𝜀𝜀𝑖𝑖𝑖𝑖 = + + + + +
𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 1𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖−1 2𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 3𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 4𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖−1 𝑟𝑟 𝛼𝛼 + 𝛽𝛽 𝑀𝑀𝑀𝑀𝑀𝑀 𝛽𝛽 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝛽𝛽 𝐵𝐵𝐵𝐵 𝛽𝛽 𝑀𝑀 𝑀𝑀𝑀𝑀 (4.5)
𝛽𝛽5𝑖𝑖𝑖𝑖𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖 𝜀𝜀𝑖𝑖𝑖𝑖 = + + + + +
𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 1𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖−1 2𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 3𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 4𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖−1 𝑟𝑟 𝛼𝛼 + 𝛽𝛽 𝑀𝑀𝑀𝑀𝑀𝑀 + 𝛽𝛽 𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆 𝛽𝛽 𝐵𝐵𝐵𝐵 𝛽𝛽 𝑀𝑀 𝑀𝑀𝑀𝑀 (4.6)
𝛽𝛽5𝑖𝑖𝑖𝑖𝐿𝐿𝐿𝐿𝐿𝐿𝑖𝑖𝑖𝑖 𝛽𝛽6𝑖𝑖𝑖𝑖𝑟𝑟𝑖𝑖𝑖𝑖−1 𝜀𝜀𝑖𝑖𝑖𝑖 In the above equations, is the logarithmic return of stock i related to
𝑖𝑖𝑖𝑖 month m, is the maximum𝑟𝑟 daily return of stock i over the previous
𝑖𝑖𝑖𝑖−1 month (m-𝑀𝑀1)𝑀𝑀𝑀𝑀 and in the log market capitalization of stock i in the
𝑖𝑖𝑖𝑖 beginning of the year𝑆𝑆𝑆𝑆 𝑆𝑆𝑆𝑆y. In addition, is the book to market ratio of stock i in
𝑖𝑖𝑖𝑖 the beginning of the year y and 𝐵𝐵𝐵𝐵 is the performance of the stock i over
𝑖𝑖𝑖𝑖−1 the previous year y-1. is the𝑀𝑀 percentage𝑀𝑀𝑀𝑀 of the non-zero return days over the
𝑖𝑖𝑖𝑖 year y and is the𝐿𝐿𝐿𝐿𝐿𝐿 return of stock i over the previous month m-1 which
𝑖𝑖𝑖𝑖−1 captures the𝑟𝑟 effect of short-term reversals. Significant coefficients in these regression equations would imply that the relevant factor has actual explanatory power and can explain partial variations of stock returns.
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Table 4.15 shows the results from the panel regression analysis. Panel regression equations (4.3) to (4.6) have been performed on data related to each country separately. The results indicate that the MAX effect is not only persistent but also dominant when including other risk factors such as size, book to market ratio, momentum, liquidity and short-term return reversals. The MAX effect is the main variable to explain the variation in cross-section of stock returns. This is the case for all nine advanced emerging markets of the sample. The results are strong and robust at the 1% level even in Mexico in which the MAX effect does not exist in the time series analysis. The results of this study provide evidence that the
MAX is an important risk factor that cannot be explained by previous anomalies.
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Table 4.15: Panel regression results The table shows the results from the panel regression. The dependant variable is the daily individual stock returns. For each country, four different panel regressions have been performed separately. The MAX(-1) has been used as an independent variable, which is the maximum daily return over the previous month. The logarithm of the market capitalization and book to market ratio of each individual company in the beginning of the calendar year has been used as independent variables to control for size and value risk factors. In addition, stocks have been ranked based on their performance over the previous calendar year and the percentage of traded days and rank them to create the momentum and liquidity factor similar to size and value factors, and use them as independent variables in the regression. Lastly, the Return(-1) has been included as an independent variable, which is the return of each individual stock over the previous month to control for the short-term return reversals. ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively.
c max(-1) ln(size) BM momentum liquidity return(-1) R-squared 0.013865 -0.066544 0.001625 0.000** 0.000**
0.049731 -0.103614 -0.005027 0.00019 0.005772
0.000** 0.000** 0.000** 0.095 Brazil 0.036311 -0.145256 -0.001815 0.0000923 -0.008974 -0.010371 0.005384
0.000** 0.000** 0.000** 0.494 0.000** 0.028*
0.040707 -0.172095 -0.002007 0.0000606 -0.009368 -0.012428 0.054971 0.007553 0.000** 0.000** 0.000** 0.652 0.000** 0.008** 0.000** 0.005874 -0.349448 0.010508 0.005** 0.000**
-0.007979 -0.437618 0.002463 0.000587 0.024061
Czech Republic 0.246 0.000** 0.002** 0.051
-0.010128 -0.556813 0.007307 0.000696 -0.010042 -0.054755 0.040304
0.308 0.000** 0.000** 0.060 0.009** 0.000**
-0.010869 -0.536152 0.00698 0.000664 -0.009737 -0.050682 0.050705 0.04288
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0.274 0.000** 0.000** 0.072 0.011* 0.000** 0.000** 0.01318 -0.243786 0.020268 0.000** 0.000**
0.017421 -0.251137 -0.000523 0.000323 0.022122
0.069 0.000** 0.582 0.261 Hungary 0.025626 -0.242192 0.003169 0.000262 0.001717 -0.06062 0.011565
0.015* 0.000** 0.004** 0.278 0.577 0.000**
0.02874 -0.265078 0.002915 0.000263 0.001113 -0.05956 0.035419 0.012752 0.007** 0.000** 0.009** 0.275 0.718 0.000** 0.007** 0.003337 -0.089743 0.001847 0.000** 0.000**
0.005351 -0.07284 -0.001216 0.003728 0.00373
0.001** 0.000** 0.000** 0.000** Malaysia 0.021915 -0.075588 0.002167 0.003518 -0.01584 -0.052537 0.008706
0.000** 0.000** 0.000** 0.000** 0.000** 0.000**
0.024248 -0.095588 0.001802 0.003328 -0.016026 -0.050611 0.029749 0.009371 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.000** 0.011229 -0.067283 0.001075 0.000** 0.000**
0.008419 -0.064685 0.000193 0.001062 0.002045 Mexico 0.014* 0.000** 0.602 0.000**
-0.005331 -0.050915 0.000641 0.000771 -0.002747 0.009452 0.001514
0.271 0.002** 0.149 0.002** 0.130 0.045*
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-0.004609 -0.056464 0.000611 0.00076 -0.002856 0.009173 0.010689 0.001608
0.343 0.001** 0.170 0.002** 0.116 0.052 0.133 0.000624 -0.142017 0.003229 0.538 0.000**
0.005539 -0.141817 -0.001015 0.001322 0.003761
0.018* 0.000** 0.007** 0.000** Poland 0.007975 -0.126742 -0.001238 0.001215 0.003773 -0.000693 0.003369
0.056 0.000** 0.004** 0.000** 0.001** 0.897
0.013594 -0.158585 -0.001516 0.001183 0.002875 -0.002736 0.058204 0.006014 0.001** 0.000** 0.001** 0.000** 0.013* 0.607 0.000** 0.012063 -0.135745 0.007336 0.000** 0.000**
0.021995 -0.156093 -0.001343 0.001158 0.00911
0.000** 0.000** 0.000** 0.000** South Africa 0.019858 -0.193732 0.000959 0.00122 0.009369 -0.020985 0.014694
0.000** 0.000** 0.047* 0.002** 0.000** 0.000**
0.020057 -0.197875 0.00098 0.001142 0.009475 -0.021032 -0.006606 0.015254 0.000** 0.000** 0.043* 0.003** 0.000** 0.000** 0.127 0.009333 -0.200508 0.001593 0.000** 0.000** Taiwan 0.035507 -0.241224 -0.003335 0.004117 0.005537
0.000** 0.000** 0.000** 0.000**
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0.030328 -0.25633 -0.002491 0.003496 -0.009367 0.000401 0.006314
0.000** 0.000** 0.000** 0.000** 0.000** 0.896
0.033887 -0.329173 -0.002494 0.003474 -0.009532 0.000388 0.057173 0.009463 0.000** 0.000** 0.000** 0.000** 0.000** 0.900 0.000** 0.018231 -0.031983 0.000075 0.000** 0.021*
0.03225 -0.115063 -0.00377 0.004079 0.004943
0.000** 0.000** 0.000** 0.000** Turkey 0.010511 -0.101977 -0.003239 0.003555 -0.018777 0.028143 0.010524
0.146 0.000** 0.000** 0.000** 0.000** 0.003**
0.012859 -0.133699 -0.003344 0.003471 -0.018533 0.028049 0.023318 0.010998 0.076 0.000** 0.000** 0.000** 0.000** 0.003** 0.000**
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The results reported in this study provide evidence that the MAX effect is more persistent in advanced emerging markets compared to previous studies on the US and other developed markets (see for instance, Walkshäusl (2014) who studied European stock markets). This stronger MAX effect in advanced emerging markets may have been caused by two reasons, both of which support the mispricing hypothesis for the MAX effect. First, the advanced emerging markets examined in this study are characterised by a lower proportion of institutional investors compared with developed markets (Voronkova & Bohl,
2005). The finding of a stronger MAX effect within markets that have fewer institutional investors supports the finding of Fong and Toh (2014), who suggest that the MAX effect is strongly dependent on investor sentiment and is more pronounced among stocks with a higher proportion of individual investors.
Second, advanced emerging markets exhibit higher levels of limits to arbitrage compared with developed markets. For instance, there are more restrictions on short selling and higher idiosyncratic volatility in emerging markets. Moreover, many emerging markets are not yet fully integrated with developed markets and there are restrictions on imposing a further limit to investors attempting to arbitrage away the MAX anomaly.
4.4.The MAX effect and the limits to arbitrage hypothesis
According to Shleifer and Vishny (1997), levels of mispricing should be higher in markets that are characterised with greater levels of limits to arbitrage. Therefore, if the MAX effect has been caused by mispricing, there should be a meaningful relation between the MAX effect and the level of limits to arbitrage over time.
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This section examines the hypothesis that the MAX effect is related to variations in the degree of limits to arbitrage across advanced emerging markets. More specifically, this study examines the relationship between the MAX effect and four variables that are most affective on the degree of limits to arbitrage in each of the stock markets in the sample. To test this proposition, four variables are used to proxy for time series variation in the level of limits to arbitrage across advanced emerging markets: dividend yields, transaction costs, volatility and liquidity.
Pontiff (1996) argues that dividends decrease the arbitragers’ holding costs. Therefore, higher dividend yields should cause lower levels of limits to arbitrage and hence, lower returns on the MAX investment strategy. On the other hand, high transaction costs will increase the costs of arbitragers and will act as a limit to arbitrage (Barberis & Thaler, 2003). Therefore, higher transaction costs should prevent arbitragers from exploiting the MAX effect. The proportion of zero return days has been used as a proxy for transaction costs, as suggested by
Lesmond et al. (1999). Further, risk averse arbitragers avoid riskier and more volatile markets, and high market volatility makes arbitrage opportunities less attractive (Shleifer & Vishny, 1997). According to this argument, high market volatility will increase the noise trader risk, which can act as a limit to arbitrage.
Therefore, the returns on the MAX investment strategy should be positively related to volatility. Finally, low levels of liquidity increase the risk of taking positions in arbitrage opportunities. Therefore, higher liquidity decreases the risk of arbitragers and would have negative relation with the MAX effect.
To study the relation between the MAX effect and the above variables, the following time series regression equations has been used:
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= + + ( ) + ( ) + ( ) +
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖 1𝑖𝑖 𝑚𝑚𝑚𝑚 𝑓𝑓𝑓𝑓 2𝑖𝑖 𝑡𝑡 3𝑖𝑖 𝑡𝑡 4𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 ∆ 𝑟𝑟 𝛼𝛼 𝛽𝛽 �𝑟𝑟 − 𝑟𝑟 � 𝛽𝛽 𝑆𝑆𝑆𝑆𝑆𝑆 𝛽𝛽 𝐻𝐻𝐻𝐻𝐻𝐻 𝛽𝛽 𝐷𝐷𝐷𝐷 (4.𝜀𝜀 7)
= + + ( ) + ( ) + ( ) +
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖 1𝑖𝑖 𝑚𝑚𝑚𝑚 𝑓𝑓𝑓𝑓 2𝑖𝑖 𝑡𝑡 3𝑖𝑖 𝑡𝑡 4𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 ∆𝑟𝑟 𝛼𝛼 𝛽𝛽 �𝑟𝑟 − 𝑟𝑟 � 𝛽𝛽 𝑆𝑆𝑆𝑆𝑆𝑆 𝛽𝛽 𝐻𝐻𝐻𝐻𝐻𝐻 𝛽𝛽 𝑃𝑃 𝑃𝑃𝑃𝑃𝑃𝑃 (4.8)𝜀𝜀
= + + ( ) + ( ) + ( ) +
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖 1𝑖𝑖 𝑚𝑚𝑚𝑚 𝑓𝑓𝑓𝑓 2𝑖𝑖 𝑡𝑡 3𝑖𝑖 𝑡𝑡 4𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 ∆ 𝑟𝑟 𝛼𝛼 𝛽𝛽 �𝑟𝑟 − 𝑟𝑟 � 𝛽𝛽 𝑆𝑆𝑆𝑆𝑆𝑆 𝛽𝛽 𝐻𝐻𝐻𝐻𝐻𝐻 𝛽𝛽 𝑉𝑉𝑉𝑉𝑉𝑉 (4.𝜀𝜀9)
= + + ( ) + ( ) + ( ) +
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖 1𝑖𝑖 𝑚𝑚𝑚𝑚 𝑓𝑓𝑓𝑓 2𝑖𝑖 𝑡𝑡 3𝑖𝑖 𝑡𝑡 4𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 ∆ 𝑟𝑟 𝛼𝛼 𝛽𝛽 �𝑟𝑟 − 𝑟𝑟 � 𝛽𝛽 𝑆𝑆𝑆𝑆𝑆𝑆 𝛽𝛽 𝐻𝐻𝐻𝐻𝐻𝐻 𝛽𝛽 𝑇𝑇 𝑇𝑇 (4.𝜀𝜀 10)
= + + ( ) + ( ) + ( ) +
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 𝑖𝑖𝑖𝑖 1𝑖𝑖 𝑚𝑚𝑚𝑚 𝑓𝑓𝑓𝑓 2𝑖𝑖 𝑡𝑡 3𝑖𝑖 𝑡𝑡 4𝑖𝑖 𝑖𝑖𝑖𝑖 ∆𝑟𝑟 ( 𝛼𝛼) + 𝛽𝛽(�𝑟𝑟 )−+𝑟𝑟 � ( 𝛽𝛽 )𝑆𝑆𝑆𝑆𝑆𝑆+ 𝛽𝛽 𝐻𝐻𝐻𝐻𝐻𝐻 𝛽𝛽 𝐷𝐷𝐷𝐷(4.11)
𝛽𝛽4𝑖𝑖 𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑖𝑖 𝛽𝛽4𝑖𝑖 𝑉𝑉𝑉𝑉𝑉𝑉𝑖𝑖𝑖𝑖 𝛽𝛽4𝑖𝑖 𝑇𝑇𝑇𝑇𝑖𝑖𝑖𝑖 𝜀𝜀𝑖𝑖𝑖𝑖 In the above equations, is the difference between the return of the
𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀𝑀 extreme portfolios sorted based∆ 𝑟𝑟on MAX effect in month t and is the three-
𝑓𝑓𝑓𝑓 month Treasury Bill Rate of the country under study in month t. 𝑟𝑟 is the return
𝑚𝑚𝑚𝑚 of the constructed market index in month t. and are𝑟𝑟 the differences
𝑡𝑡 𝑡𝑡 between returns of the extreme portfolios sorted𝑆𝑆𝑆𝑆𝑆𝑆 based on𝐻𝐻 size𝐻𝐻𝐻𝐻 and book to market ratios in month t and is the error term. , , and are the
𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 𝑖𝑖𝑖𝑖 dividend yield, the arithm𝜀𝜀 etic average proportion𝐷𝐷𝐷𝐷 𝑃𝑃of𝑃𝑃 zero𝑃𝑃𝑃𝑃 return𝑉𝑉𝑉𝑉𝑉𝑉 days of𝑇𝑇 𝑇𝑇individual stocks as a proxy for transaction cost at the market level, the one month ahead volatility estimates of returns of the MAX strategy using GARCH model3 and the turnover ratio (trading volume divided by market value) in month t.
3This study also uses the idiosyncratic volatility of stocks as well as the total market volatility as other estimates of volatility in each market, perform the same analysis, and find similar results.
184 | Page
Table 4.16 shows the coefficients and the p-value related to the four influential variables that are used in equations (4.7) to (4.11). Panel A shows the results when the variables are included to the Fama and French (1993) three-factor model separately, while panel B shows the results when all variables are included together in the same model. Consistent with the expectation, this study finds signs of negative time series relation between dividend yield and the MAX effect in
Malaysia and Mexico. This means that the MAX effect is related to dividend yield, the proxy for arbitrage opportunity. Higher dividend yield decreases the holding cost of stocks; hence, it would be cheaper and easier for arbitragers to exploit the opportunity. However, this chapter also finds a significant positive relation between the two in Brazil’s and the Czech Republic’s equal weighted portfolios.
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Table 4.16: Time series regression results for the relation between the MAX effect and the proxies for limits to arbitrage The table shows the results for the time series regression in which the dependant variable is the returns from the MAX strategy. Panel A shows the coefficients and p-values when we perform time series regression and include each of the proxy variables to the Fama and French (1993) three-factor model separately as independent variables (i.e. four independent variables: market beta, size, value and the mentioned variable). Panel B shows the results when we include all variables together in the Fama and French (1993) three-factor model (i.e. seven independent variables: market beta, size, value and all four mentioned variables). P0RD is the proxy for the market level transaction cost, which is the arithmetic average proportion of zero return days of individual firms. Vol is the one month ahead volatility estimates of returns of the MAX strategy using GARCH model and TV/MV is the turnover ratio (trading volume divided by market value) in month t. ***, ** and * indicate that the relevant mean return is significantly different from zero at the 0.01, 0.05 and 0.10 level, respectively.
Czech South Brazil Hungary Malaysia Mexico Poland Taiwan Turkey Republic Africa Equal Weighted Panel A Dividend Yield 0.0650 0.1048 -0.0154 -0.0267 -0.1411 0.0153 -0.0509 0.0017 -0.0070 0.0250** 0.0030*** 0.7450 0.3130 0.1370 0.5790 0.1940 0.9170 0.8000
P0RD -0.0454 -0.0195 -0.0497 0.0469 -0.0745 0.0865 0.0498 -0.0554 0.1591 0.1870 0.5920 0.5190 0.0550* 0.0870* 0.0210** 0.0430** 0.6060 0.1320
Vol 0.0076 0.3585 0.1675 0.0423 -0.1267 -0.3598 0.3664 0.0462 0.0318 0.9620 0.0010*** 0.2730 0.3560 0.0550* 0.0210** 0.3890 0.7220 0.8080
TV/MV -0.0025 -0.0235 0.3009 -0.0008 -0.0072 -0.0028 0.0058 0.0002 0.0003 0.5970 0.8120 0.4670 0.3450 0.0460** 0.3430 0.2120 0.8770 0.1330 Panel B Dividend Yield 0.0554 0.0510 -0.0241 -0.1246 -0.1857 -0.0105 -0.0739 0.0038 0.0075 0.3180 0.2070 0.6380 0.0020*** 0.0880 0.7700 0.0960 0.8290 0.8040
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P0RD -0.0342 0.0019 -0.0054 0.1608 -0.0936 0.0715 0.0232 -0.0589 0.1500 0.4140 0.9710 0.9500 0.0000*** 0.1590 0.0930 0.4380 0.6160 0.1610
Vol 0.0469 0.2865 0.1721 0.1408 -0.0933 -0.2893 0.2793 0.0360 0.0368 0.7960 0.0190** 0.2660 0.0070*** 0.2620 0.0870 0.5110 0.8130 0.7870
TV/MV -0.0050 -0.0639 0.2800 -0.0008 0.0042 -0.0013 0.0079 -0.0001 0.0003 0.3590 0.6550 0.5190 0.4000 0.5130 0.7200 0.1870 0.9540 0.1700 Value Weighted Panel A Dividend Yield 0.0678 0.0571 0.0830 -0.0521 -0.3812 0.0275 -0.0705 0.0322 -0.0723 0.1650 0.1920 0.2920 0.2000 0.0270** 0.6190 0.4040 0.2080 0.2810
P0RD -0.0291 -0.0062 0.0650 0.0267 -0.0098 0.2230 -0.0009 0.1006 -0.2958 0.6160 0.8920 0.6070 0.4810 0.9030 0.0030*** 0.9860 0.5510 0.2780
Vol 0.1207 0.2195 0.4305 -0.0057 -0.0009 0.2803 0.0594 -0.1976 -0.0119 0.4290 0.0350** 0.0030*** 0.9070 0.9890 0.1700 0.7020 0.1430 0.8720
TV/MV -0.0044 -0.0795 -0.1179 -0.0036 -0.0054 -0.0090 0.0104 -0.0048 0.0000 0.6000 0.5220 0.8640 0.0060*** 0.4000 0.1200 0.2680 0.0540** 0.9460 Panel B Dividend Yield 0.0908 0.0230 0.0134 -0.1106 -0.4398 0.0182 -0.1471 0.0238 -0.0801 0.3370 0.6080 0.8740 0.0770 0.0290** 0.7920 0.1260 0.3810 0.3000
P0RD -0.0857 0.0468 -0.0285 0.1428 -0.1178 0.2010 -0.0586 -0.0098 -0.2896 0.2900 0.4730 0.8400 0.0120** 0.3340 0.0150** 0.3540 0.9560 0.3100
Vol 0.1763 0.2241 0.4393 0.1095 0.0104 0.3075 0.6708 -0.1365 0.0317 0.3100 0.0410** 0.0040*** 0.0530 0.8900 0.1310 0.4650 0.3320 0.6970
TV/MV -0.0081 -0.2239 -0.0512 -0.0042 0.0064 -0.0095 0.0206 -0.0039 -0.0001
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0.3930 0.2090 0.9410 0.0080*** 0.5780 0.1660 0.0870 0.1380 0.8380
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Regarding the relation between the MAX effect and the proportion of zero return days, the proxy for transaction cost, this research finds evidence of a significant positive time series relation between the MAX effect and the proportion of zero return days in value weighted portfolios in Poland and
Malaysia and in equal weighted portfolios in Malaysia, Poland and South Africa.
This result means that the MAX effect has a significant relation with the transaction cost and the higher transaction cost increases the costs of arbitragers.
Therefore, as transaction cost increases, mispricing persists and the profit from the
MAX strategy increases. However, this study also finds a negative relation between the two in the equal weighted portfolios in Mexico.
Table 4.16 also shows that the relation between the volatility estimates used in this study and the MAX effect is positive and significant in the Czech
Republic, Hungary and Malaysia. This means that the MAX effect has a significant relation with the estimates of one-month lagged volatility of the MAX portfolio. If the perception of one-month ahead volatility increases, taking positions for arbitragers would become more costly; and therefore, arbitragers are reluctant to take such risky positions. Therefore, the mispricing would stay in the market and the return of a MAX strategy would be higher. However, this research also finds a significant negative relation between one-month ahead volatility and the MAX effect in Mexico’s and Poland’s equal weighted portfolios.
To study the relation between the MAX effect and market liquidity, the market turnover over market capitalization has been used as proxy for liquidity.
This study finds a significant negative relation between the liquidity proxy and the
MAX effect in the equal weighted portfolios in Mexico and in the value weighted
189 | Page portfolios in Malaysia and Taiwan. It can be argued that, as the liquidity in the stock market decreases, the risk of arbitragers’ positions increases and they are reluctant to take positions. Therefore, the mispricing remains in the market and the MAX strategy continues to have abnormal returns.
Taken as a whole, the results of this study show that the relation between the MAX effect, and at least one of the proxies for limits to arbitrage, is significant in eight out of nine advanced emerging markets with the exception of
Turkey. Therefore, there is a time series relation between the MAX effect and the limits to arbitrage in the advanced emerging countries. This robust relation confirms the argument of Zhong and Gray (2016) that the MAX effect may be attributable to mispricing, which occurs due to investors’ preferences for assets with lottery-type payoffs. The results show that the MAX effect increases during periods when the limits to arbitrage are higher due to the significant impediments that prevent this mispricing from being arbitraged away during these periods.
4.5.Conclusion
This chapter examines the MAX effect in the context of advanced emerging markets, by studying monthly stock returns over 20 years from 1995 to 2015. This study finds strong evidence of a MAX effect in the cross-section of emerging market returns that is robust to size, book to market ratio, momentum, liquidity and return reversals. Consistent with the mispricing argument, the results show that the magnitude of the MAX effect in emerging markets where limits to arbitrage are high, is larger than the US market. For example, while Bali et al.
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(2011) report that the MAX effect generates 1.03% returns per month in the US, this study finds that the MAX effect generates statistically significant returns of
2.784%, 2.761% and 2.184% per month in the Czech Republic, Hungary and
South Africa respectively. These results cast doubt on the semi-strong form market efficiency of advanced emerging markets. Furthermore, regression results indicate that the return from the MAX strategy cannot be explained by existing common risk factors such as size, book to market ratio, momentum, liquidity and return reversals.
This study proposes possible explanations for the results. First, the stronger MAX effect in emerging markets might be because of the lower proportion of managed funds and thus, a higher proportion of unsophisticated and less educated individual investors in emerging markets compared to developed markets (Voronkova & Bohl, 2005). These individual investors are more willing to buy lottery-type assets and cause more mispricing (Fong & Toh, 2014; Kumar,
2009). Such under-diversification and investors’ preference for lottery-type assets are important contributors of the MAX effect. Therefore, any mispricing related to lottery-type assets would be greater in emerging markets.
Second, the higher MAX effect in advanced emerging markets might be due to the higher levels of limits to arbitrage in these markets. The results of this chapter provide evidence that the time series of MAX effect returns are related to a variation in measures of market-level limits to arbitrage in advanced emerging markets. This means that the MAX effect is more pronounced over periods characterised by higher trading costs and restrictions, which prevent mispricing opportunities from being arbitraged away. Hence, the results can be interpreted as
191 | Page providing support to the argument put forward by Zhong and Gray (2016) that the
MAX effect is primarily due to mispricing, which occurs because of investors’ preference for lottery-type assets.
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Chapter 5 : Conclusion
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5.1.Introduction
The process of financial liberalization in advanced emerging stock markets had started in the late 1980s and early 1990s. This process has contributed to integration of advanced emerging markets with global markets and hence has become of greater interest to international investors. Despite the move toward financial integration, these markets still have characteristics that are different compared to developed markets. For instance, these markets exhibit higher average returns and higher volatility compared to developed markets (Bekaert &
Harvey, 1997). Consequently, they can provide a unique set of opportunities for investors. While investors can diversify away the higher volatility in these markets, they can also benefit from higher returns (Bekaert & Harvey, 2014).
Moreover, emerging market returns have considerably lower correlation with returns in developed markets. Thus, a substantial degree of diversification can be achieved through international diversification that includes emerging markets as part of the overall portfolio (Harvey, 1995). As a result, there has been an upward trend in the popularity of investments in these markets in the last two decades
(Bekaert & Harvey, 2014).
Despite this popularity, there is a high level of information asymmetry for investors seeking to access emerging stock markets. Given the different market characteristics that have been observed across emerging markets may result in different return and volatility patterns, standard asset pricing models may have inadequate explanatory power for the variation of stock returns in emerging markets. This thesis fills this gap by undertaking a comprehensive examination of
195 | Page the market characteristics and institutional features of advanced emerging markets and using the unique characteristics identified to motivate an analysis of stock return anomalies in these markets.
Despite the growing interest of investors in emerging markets, and the fact that these markets are different compared to developed markets, very few empirical studies have examined the stock returns and volatility characteristics in emerging markets. While there is paucity of academic evidence in asset pricing literature regarding emerging markets, this thesis studies the characteristics of these markets and examines six anomalies that have been introduced in developed markets’ literature. More specifically, this thesis examines the month of the year, other January, day of the week, holiday, week of the year and the MAX effect in nine advanced emerging markets.
5.2.Key findings
This thesis finds that advanced emerging markets have lower market capitalization, lower liquidity, a weaker corporate governance environment and more volatile foreign exchange rates compared to developed markets. For instance, the average market capitalization of advanced emerging markets is 338.3 billion US dollars and the average turnover ratio of advanced emerging markets is
62.5% in 2015; whereas the US market capitalization is 22,521.1 billion US dollars and the turnover ratio of US market is 165.1%. Moreover, the foreign exchange rate of the currency in advanced emerging markets is more volatile compared to more developed markets. In addition, emerging markets have other
196 | Page characteristics such as restrictions on capital flows, short selling constraints and extra political and economic uncertainty. These unique characteristics can affect returns and volatility from the perspective of an international investor. As the result of these different market characteristics, the limit to arbitrage is higher in advanced emerging markets. Consequently, these markets expected to have higher levels of mispricing and more pronounced anomalous returns compared to developed markets.
In the first empirical study of this thesis, five seasonal anomalies have been examined in the context of advanced emerging markets. This study is relevant for two reasons. First, there is debate regarding the relative efficiency of emerging markets compared with developed markets (for instance see Bekaert and
Harvey (2014) and Griffin et al. (2010)), and examining the performance of seasonal anomalies provides a comprehensive test of market efficiency that is largely unaffected by the joint test problem. Second, emerging markets contain differences regarding their time zones, tax year end and public holiday structure that allow for a direct test of the existing theories of why seasonal anomalies are observed in developed markets. The specific contexts of this study are the nine countries classified as being advanced emerging by the Financial Times Stock
Exchange as of 2014: Brazil, the Czech Republic, Hungary, Malaysia, Mexico,
Poland, South Africa, Taiwan and Turkey. The sample period used in this study is different for each country because of data limitations and ranges between 1973 and 2014 for South Africa and 1994 and 2014 for Brazil and Poland. time series regressions are used to examine patterns in daily, weekly and monthly aggregate market returns across these countries. Moreover, where the volatility of stock
197 | Page returns is not constant, the GARCH model with similar dummy variables has been used to capture the seasonality in volatility of stock returns.
The results of the month of the year analysis provide evidence of monthly variation in returns across advanced emerging markets. However, these markets exhibit different return patterns compared to developed markets where January returns are higher. The results show that stock returns are higher in December compared to other months during the year in seven out of the nine advanced emerging markets, with no evidence of higher returns in January. Therefore, the results of this analysis are consistent with previous evidence that the January effect has been diminished in international markets (Darrat et al., 2011).
Moreover, the results can be viewed as being at odds with the tax loss-selling hypothesis of Branch (1977), in which investors tend to sell their losing stocks in
December. Furthermore, there is no evidence of seasonal variation in volatility across monthly stock returns, which indicates that the abnormal returns in
December cannot be described by risk-based explanations.
The results of the analysis regarding the other January effect show that this anomaly is not persistent in advanced emerging markets; confirming that this effect is not an international phenomenon and might be the result of data-snooping in the US market (Bohl & Salm, 2010). Alternatively, the lack of evidence supporting the existence of this anomaly across emerging markets may be viewed as being consistent with the institutional investment committee theory of Little and Albrecht (2006), which argues that January returns are a barometer for the rest of the year, given institutional investors tend to make investment decisions for the rest of the year at the end of January. Given emerging markets are
198 | Page characterized by very few institutional investors compared to developed markets, anomalies caused by the actions of these investors should not be as strong in the context of emerging markets.
The results of the analysis on daily returns show that the day of the week effect is persistent and strong in six out of the nine advanced emerging markets.
More specifically, Monday returns tend to be lower while Friday returns tend to be higher compared to other days of the week. This daily returns pattern is similar to the previous empirical studies on developed markets (Lakonishok & Levi,
1982). However, the results of this analysis show no sign of the lower Tuesday returns for the markets located in the Far East (Malaysia and Taiwan), which is inconsistent with the argument of Aggarwal and Rivoli (1989) in which the time zone differential affects the day of the week effect in Eastern markets.
Furthermore, this study finds no evidence of time-varying volatility; and therefore, does not support the argument of Campbell and Hentschel (1992) that daily variation in volatility will increase the required rate of returns and cause higher average Friday returns.
The results from the examination of the holiday effect show that, after controlling for the day of the week effect, evidence of positive abnormal returns on public holidays is persistent, especially post-holidays, which exhibit abnormal daily returns in seven out of nine advanced emerging markets. The existence of the holiday effect in a broad sample of emerging markets shows that this effect is an international phenomenon and is independent to the characteristics of holidays.
As short-selling is limited in emerging markets, the weaker pre-holiday effect in advanced emerging markets may be viewed as being supportive of the inventory
199 | Page adjustment hypothesis of Ariel (1990) in which, the pre-holiday effect is caused by the investors’ tendency to close the short-sold positions before holidays.
The results from the weekly returns analysis show evidence that is consistent with Levy and Yagil (2012) who report that, in developed markets, returns tend to be higher in the 44th week of the year. Across the sample of advanced emerging markets examined in this thesis, there is evidence of statistically higher average returns compared to other weeks during the year in eight out of nine markets, with the exception of Malaysia. This result can support the seasonal affective disorder hypothesis of Kamstra et al. (2003) because
Malaysia in the only equatorial country and has no daylight saving system. On the other hand, the results display similar return patterns for countries located in the northern and southern hemispheres, which cannot be explained by the seasonal affective disorder argument. The results also show that the seasonality in volatility of returns exists in South Africa, which shows that the stock returns during the
44th week of the year have higher volatility compared to other weeks. Therefore, it may be argued that the higher returns during 44th week of the year in South
Africa can be explained by investors’ higher required rates of return to compensate for higher volatility in this week.
Stocks in emerging markets also tend to have higher levels of idiosyncratic volatility and a different distribution of returns compared with developed markets
(Bekaert & Harvey, 2014). Given these characteristics, it would be expected that the relationship between idiosyncratic risk, skewness and returns is different in developed markets compared with emerging markets. To directly test this question, the second empirical study examined the MAX effect, which is the
200 | Page phenomenon reported in developed markets that the magnitude of the maximum daily return in the previous month is negatively related to returns in the subsequent month. The firm level daily returns as well as yearly market capitalization and book to market ratio has been used to perform the univariate and bivariate portfolio sorting, time series regression and panel regression analysis. The sample period used in this study comprises 21 years from 1995 to
2016.
The results of this study show that the MAX effect is prevalent in advanced emerging markets and can produce abnormal returns that cannot be explained by commonly existing risk factors such as size, book to market ratio, momentum, liquidity, market beta and short-term return reversals. The results of the MAX effect in advanced emerging markets found in this study are stronger compared to the results of Bali et al. (2011) in the US market. The stronger MAX effect might be due to the existence of a higher proportion of individual unsophisticated investors and higher levels of limits to arbitrage in emerging markets compared to developed markets.
This study has also examined the time series relation between the MAX effect and four variables that can be used as proxy for time-variation in the degree of limits to arbitrage. The results show that the MAX effect has significant time series relation with at least one of these proxies significantly related to variation in
MAX portfolio returns in eight out of nine advanced emerging markets. The results of this study can support the argument of Zhong and Gray (2016) that the
MAX effect is due to mispricing attributed to the investors’ preference for stocks
201 | Page with lottery-type pay-offs and is higher during the times that there are higher levels of limits to arbitrage in the market.
5.3.Implications of key findings
The results reported in this thesis provide important implications for both academics and investors alike. First, given the paucity of empirical studies in the context of emerging stock markets, this thesis will result in a better understanding of advanced emerging stock markets. This can result in the development of asset pricing models that are more powerful predictors of expected returns in the context of emerging markets. More specifically, this study has performed tests regarding various kinds of anomalies including five different seasonal anomalies, the MAX effect, size, book to market ratio, momentum and liquidity. Therefore, this thesis will be useful for improved investment decision making in advanced emerging markets. For instance, in order to generate abnormal returns, investors may exploit the month (week) of the year effect in advanced emerging markets by taking long positions in the beginning of December (44th week of the year) and holding them until the end of the month (week). Similarly, to exploit the day of the week and holiday effect, investors should have long positions on Fridays and pre- and post-holidays. In addition, investors can make monthly abnormal profits from cross sectional mispricing in the market caused by the MAX effect by taking long (short) position in the portfolio of low (high) MAX stocks. For instance, by using this strategy, a monthly abnormal return of 2.784% and 2.761% can be achieved in the Czech Republic and Hungary respectively.
202 | Page
Second, advanced emerging markets provide an ideal laboratory to perform out-of-sample tests of the anomalies that have been identified in developed markets and their associated explanations. This is because emerging countries have unique market characteristics that can affect the patterns of stock returns and volatility. As a result, this study can test the theoretical explanations that have been proposed for these anomalies. Therefore, the findings of this study will result in better understanding of characteristics and underlying contributors that cause these anomalous returns.
Third, while there has been debate regarding the efficiency of different stock markets in academic literature; this study provides a test of the efficiency of the advanced emerging markets. The results of this thesis show that the anomalous returns are generally higher in advanced emerging markets compared to developed markets. In particular, the result of Chapter 3 provides evidence that seasonal patterns in returns and volatility are not prevalent and supports the weak form efficiency of advanced emerging markets. However, stronger anomalous returns related to the MAX effect compared to developed markets found in
Chapter 4 are inconsistent with the semi strong form efficiency of advanced emerging markets. Therefore, this study provides evidence that these markets are less than perfectly efficient, supporting the argument of Bekaert and Harvey
(2014). In fact, advanced emerging markets exhibit a higher degree of mispricing and limits to arbitrage compared to developed markets. These results can be of use for policy makers across developing markets to target their equity market regulation toward achieving a more efficient stock market by mitigating market frictions and information asymmetry. For instance, by removing the existing
203 | Page foreign ownership restrictions in Malaysia and Mexico, permitting stock lending in the Czech Republic, Hungary, Malaysia and Taiwan and permitting short- selling in the Czech Republic, Malaysia, Mexico and Taiwan, these policy makers can reduce market frictions and achieve more efficient markets. In addition, more comprehensive company disclosures may mitigate information asymmetry and improve market efficiency in advanced emerging markets.
5.4.Limitations and directions for future research
This thesis examines the statistical significance of anomalous returns in advanced emerging markets. It is essential to understand that the abnormal returns calculated in this study may not be achievable in the market because executing such investment strategies can incur considerable transaction costs, which can negate the estimated profits. Due to a lack of data related to transaction costs, and differences in the magnitude of transaction costs incurred by investors, this research is not able to go beyond the statistical significance to also examine the economic significance of the anomalous returns reported.
There is very limited accounting data available for emerging markets. This has made this study incapable of performing additional tests and controlling for a broader set of variables that require accounting data. Moreover, the historical time period of the available data is short for emerging markets compared to developed markets. This is particularly important because using short sample periods may introduce errors in the results due to the limited number of business cycles that are included, and time series variation in expected returns. Attempts were made to
204 | Page overcome this limitation by using the longest available time series of data for each country in the sample. For example, in Chapter 4 of this thesis, the shortest sample period used in this study is 1994 to 2014 for Brazil and Poland. Similarly, in Chapter 4, firm level data is analysed for the longest period of availability, which is from 1995 to 2015.
This research provides opportunities for future study that may extend the results reported. For instance, while Chapter 3 examines the seasonality in returns and volatility of returns, future research can extend this work to also examine seasonality in trading volume in these markets. This analysis may facilitate a further investigation of the theoretical explanations of tested seasonal patterns in returns.
With respect to Chapter 4, the research reported could be extended by examining the relationship between the MAX effect and the idiosyncratic volatility of individual stocks in advanced emerging markets. This can be an important field for future research in this area because these two anomalies have similar characteristics. The high MAX stocks are likely to have high idiosyncratic volatility. Therefore, it is necessary to identify and separate the effects that these two important variables may have on stock returns.
Finally, future research might be conducted to examine the economic significance of the anomalous returns reported in this thesis. Such analysis would require researchers to access data on specific transaction costs across emerging markets that is not currently available in the public domain. Such an analysis would be a better efficiency test of emerging markets.
205 | Page
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Appendices
226 | Page
Appendix I: Descriptive statistics of market index returns in the US Dollar ** and * indicate that the relevant mean return is significantly different from zero at the 0.01 level and 0.05 level, respectively. Descriptive Statistics of Returns in US Dollar Number of Period Mean t-stat St. Dev. Minimum Maximum Skewness observations Panel (A): Daily Returns Brazil 1994-2014 5151 0.0418 1.47 2.0380 -16.2038 14.6907 -32.077 Czech Republic 1993-2014 4632 0.0465 1.93 1.6368 -15.6342 20.3210 -12.589 Hungary 1991-2014 4304 0.0138 0.43 2.1101 -19.0352 18.3787 -31.945 Malaysia 1986-2014 7368 0.0404 2.21* 1.5702 -36.7555 22.9957 -157.670 Mexico 1989-2014 6494 0.0574 2.73** 1.6961 -20.6713 13.7422 -51.273 Poland 1994-2014 5240 0.0118 0.42 2.0408 -12.4001 17.0105 -19.434 South Africa 1973-2014 10761 0.0432 2.72** 1.6473 -16.2354 16.6990 -47.394 Taiwan 1988-2014 6761 0.0185 0.80 1.8985 -12.2893 13.7416 -3.949 Turkey 1988-2014 6471 0.0463 1.25 2.9857 -26.9294 22.1638 -17.805 Panel (B): Weekly Returns Brazil 1994-2014 1041 0.1952 1.34 4.6851 -30.5388 22.1832 -66.303 Czech Republic 1993-2014 937 0.2303 1.82 3.8721 -25.1097 37.7971 10.764 Hungary 1991-2014 871 0.0701 0.42 4.9372 -41.4097 28.4194 -117.260 Malaysia 1986-2014 1489 0.1970 2.03* 3.7529 -39.1581 31.3783 -142.393 Mexico 1989-2014 1313 0.2828 2.57** 3.9874 -27.9675 19.1238 -59.926 Poland 1994-2014 1059 0.0596 0.39 4.9221 -25.6523 28.1661 -45.496 South Africa 1973-2014 2176 0.2135 2.63** 3.7895 -25.9350 23.8963 -63.758 Taiwan 1988-2014 1366 0.0883 0.72 4.5366 -24.6937 21.9024 -10.582 Turkey 1988-2014 1308 0.2211 1.14 6.9911 -35.8230 32.0849 -31.190
227 | Page
Panel (C): Monthly Returns Brazil 1994-2014 236 0.8377 1.24 10.4090 -35.2788 29.8007 -49.515 Czech Republic 1993-2014 213 1.0115 1.80 8.1801 -32.8027 19.2274 -59.868 Hungary 1991-2014 198 0.2991 0.40 10.4961 -47.3861 32.4380 -83.021 Malaysia 1986-2014 338 0.9020 1.94 8.5293 -41.8467 37.2960 -70.202 Mexico 1989-2014 298 1.1939 2.35* 8.7688 -41.7926 22.2325 -110.160 Poland 1994-2014 241 0.2557 0.36 11.1258 -43.3258 32.4478 -67.982 South Africa 1973-2014 495 0.9395 2.47* 8.4489 -41.4089 28.0980 -71.365 Taiwan 1988-2014 311 0.4015 0.68 10.3506 -45.8687 42.0071 3.398 Turkey 1988-2014 297 0.9658 1.04 15.9773 -54.5365 50.9374 -10.168
228 | Page
Appendix II: Results for the month of the year effect The table shows the regression results for equation (3.1) for January and December for all advanced emerging countries. PP is the proportion of positive monthly excess returns in the entire sample period and p-value (given in parenthesis) is the significance of binomial test. It indicates if the proportion is statistically different from 50% for all days. ** indicates significance at the 0.01 level and * indicates significance at the 0.05 level Returns denominated in US Dollar
Country Years January December All Remaining Months
α_January PP Mean PP Mean pp Brazil 20 -0.4537413 0.44 4.011072 0.65 0.6456 0.56 0.815 0.263 0.133 Czech Republic 21 1.05213593 0.65 4.953995 0.83 0.6064 0.53 0.332 0.008** 0.548 Hungary 23 1.3250837 0.69 5.790434 0.82 -0.3765 0.47 0.210 0.013* 0.482 Malaysia 28 1.16248763 0.67 4.170356 0.82 0.5516 0.57 0.122 0.001** 0.027* Mexico 25 0.58184854 0.58 2.434651 0.80 1.1306 0.58 0.541 0.004** 0.019* Poland 20 1.38815113 0.68 4.13733 0.65 -0.2432 0.53 0.167 0.263 0.481 South Africa 41 0.60111664 0.56 4.185205 0.63 0.6510 0.56 0.533 0.117 0.020* Taiwan 26 3.09892976 0.64 3.054475 0.65 -0.1356 0.49 0.230 0.169 0.804 Turkey 0.26 6.32313969 0.63 6.226676 0.60 -0.1089 0.51 0.307 0.424 0.799
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Appendix III: Results for the day of the week effect (denominated in the US Dollar) The table shows the regression results for equation (3.5) for Mondays and Fridays for all advanced emerging countries. PP is the proportion of positive daily excess returns in the entire sample period and p-value (given in parenthesis) is the significance of binomial test to show if the proportion is statistically different from 50% for all days. ** indicates significance at the 0.01 level and * indicates significance at the 0.05 level. Returns denominated in US Dollar Country Years Monday Friday All Remaining Days Mean PP Mean PP Mean PP Brazil 20 -0.0784 0.492 0.1863 0.569 0.0337 0.539 0.6399 0.00001** 0.00001** Czech Republic 21 0.0496 0.535 0.0284 0.512 0.0515 0.526 0.0355* 0.49 0.00665** Hungary 23 0.1095 0.537 0.0465 0.555 -0.0291 0.521 0.0345* 0.00134** 0.0319* Malaysia 28 -0.1371 0.493 0.1424 0.578 0.0656 0.549 0.6021 2.28E-09** 5.4E-11** Mexico 25 -0.0249 0.523 0.1340 0.565 0.0593 0.536 0.0959 0.000002** 8.8E-06** Poland 20 0.0899 0.546 0.0378 0.533 -0.0230 0.510 0.0035* 0.0371** 0.274 South Africa 41 -0.0369 0.527 0.0345 0.530 0.0728 0.539 0.0124* 0.00655** 4.0E-10** Taiwan 26 -0.0537 0.499 0.0994 0.547 0.0156 0.513 0.9783 0.000639** 0.115 Turkey 26 -0.1199 0.492 0.2002 0.543 0.0504 0.515 0.5974 0.00243** 0.0582
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Appendix IV: Regression results for pre-holiday and post-holiday effect for advanced emerging markets (denominated in the US Dollar) Coefficients have been estimated using equation (3.8) for all advanced emerging markets separately. Note that to calculate a return of a specified day; the related coefficients should be multiplied by 100. For instance, the return of a Wednesday in Brazil which is after a national holiday is (-0.126+0.1513+0.5102) = 0.5355%. ** indicates significance at the 0.01 level and * indicates significance at the 0.05 level.
Brazil Czech Republic Hungary Malaysia Mexico Poland South Africa Taiwan Turkey
Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient Coefficient
Constant -0.00126 -0.000002 0.000841 -0.00177 -0.00054 0.000677 -0.00058 -0.00058 -0.00133
0.093 1.000 0.300 0.003** 0.300 0.334 0.156 0.312 0.163
Tuesday 0.001293 0.000053 -0.0008 0.001928 0.000668 -0.00132 0.000545 0.000096 0.000529
0.201 0.946 0.472 0.013* 0.357 0.187 0.321 0.899 0.677
Wednesday 0.001553 -0.000088 -0.00157 0.002562 0.001248 -0.0013 0.001898 0.001228 0.001885
0.133 0.915 0.173 0.003** 0.104 0.192 0.005** 0.142 0.160
Thursday 0.001218 0.001027 -0.00151 0.002254 0.0013 -0.00035 0.001158 0.000723 0.003148
0.226 0.229 0.187 0.005** 0.093 0.713 0.053 0.367 0.031*
Friday 0.002808 -0.000001 -0.00048 0.002869 0.001763 -0.00045 0.000831 0.001401 0.003552
0.015* 1.000 0.663 0.001** 0.032* 0.643 0.146 0.099 0.018*
Before Holiday 0.002718 0.002617 0.00078 0.001804 0.001068 0.00004 0.000696 0.002082 -0.0019
0.097 0.087 0.677 0.078 0.407 0.977 0.508 0.151 0.478
After Holiday 0.005102 0.005429 0.004332 0.003523 0.003135 0.003466 0.00406 0.000458 0.001642
0.007** 0.003** 0.039* 0.004** 0.031* 0.048* 0.003** 0.734 0.532
Adjusted R-square 0.003501 0.003455 0.000953 0.005441 0.00124 0.000609 0.002306 0.000409 0.001201
F-statistic 3.886466 3.586121 1.663974 7.353002 2.306055 1.515942 5.01972 1.44405 2.238702
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Appendix V: Results for week 44 effect for advanced emerging markets (denominated in the US Dollar) The table shows the regression results for equation (3.9) for week 44 for all advanced emerging countries. All returns have been denoted in percentage terms. PP is the proportion of positive weekly excess returns in the entire sample period and p-value (given in parenthesis) is the significance of binomial test to show if the proportion is statistically different from 50% for all weeks. The return rank column has been calculated by sorting the weeks of the year based on weekly returns. ** indicates significance at the 0.01 level and * indicates significance at the 0.05 level.
Returns denominated in US Dollar Country Years Week 44 All Remaining Weeks Mean PP Return Rank Mean PP Brazil 20 3.6924 70 1 0.1267 54 0.115 0.012* Czech Republic 21 2.1340 61 2 0.1930 56 0.481 0.001** Hungary 23 2.4760 47 1 0.0222 55 1.000 0.002** Malaysia 28 0.4668 46 22 0.1918 57 0.851 0.000** Mexico 25 1.7401 64 5 0.2545 56 0.230 0.000** Poland 20 2.3052 70 2 0.0164 53 0.115 0.032* South Africa 41 1.7270 66 3 0.1844 55 0.060 0.000** Taiwan 26 1.8752 69 2 0.0537 53 0.076 0.037* Turkey 26 2.7372 52 3 0.1720 53 1.000 0.034*
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