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INVESTIGATION OF THE HOLIDAY EFFECT AT THE NAIROBI SECURITIES EXCHANGE (NSE)
BY
RAM OMAR
UNITED STATES INTERNATIONAL UNIVERSITY
SUMMER 2015
INVESTIGATION OF THE HOLIDAY EFFECT AT THE NAIROBI SECURITIES EXCHANGE (NSE)
BY
RAM OMAR ID No. 625465
A Project Report Submitted to the Chandaria School of Business in Partial Fulfillment of the Requirement for the Degree of Masters in Business Administration (MBA)
UNITED STATES INTERNATIONAL UNIVERSITY
SUMMER 2015 i
’ STUDENT S DECLARATION I, the undersigned, confirm that this project is my original work and has not been presented to any other institution other than United States University in Nairobi for academic award. All other works cited are properly acknowledged.
Signed ______Date: ______
Ram Omar (Id No: 625465)
This project report has been presented for examination with my consent as the appointed University supervisor.
Signed ______Date: ______
Dr. Amos Njuguna
Signed ______Date: ______
Dean, Chandaria School of Business
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ACKNOWLEDGEMENT I appreciate all the contributions and critique from my friends, mentors and lecturers. Particularly, I remain indebted to Dr. Njuguna who supervised this piece of work. His guidance and patience with me remains ingrained in memory.
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DEDICATION This piece of work is dedicated to my dear parents and colleagues in both work and school.
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ABSTRACT The purpose of this study was to investigate the existence of the holiday effect at the Nairobi Securities Exchange (NSE) with specific objectives as to determine: existence of Pre-holiday effect at the NSE, existence of Post-holiday effect at the NSE and disparities – between the pre and post holiday abnormal returns at the NSE.
The study modeled as an event study focuses on abnormal returns and cumulative abnormal returns at the NSE over seven public holidays namely: New year, Easter, Labour day, Madaraka day, Mashujaa day, Jamhuri day and Christmas holiday using companies listed at the NSE over a five year period. Regression analysis is conducted on the returns of the companies.
The study finds that most returns earned at the NSE prior to the holiday are positive when compared with the negative preholiday returns. Non parametric test results establish that positive preholiday returns are greater than normal days proportion. Regression analyses establish a statistically significant positive relationship between return and preholiday occurrence. These findings confirm the existence of the preholiday effect at the NSE.
The study finds that most stock returns generated at the NSE after the holiday are positive when compared with the negative post-holiday returns. Non parametric tests show that positive post holiday returns proportion are more than the negative post holiday returns which confirms existence of the post holiday effect. Regression analysis establish a statistically significant negative relationship confirming existence of a post holiday effect.
T-tests conducted on the returns confirm that none of the holidays has greater effects than – the other. Further, T tests on pre holiday and post holiday abnormal returns establish that there are no significant disparities between the pre and the post holiday abnormal returns.
The study recommends that the market microstructure should be aligned to enhance market price discovery and market efficiency. At the same time, further investigations should be modeled on other corporate events and national or international events that influence security returns in financial markets. v
TABLE OF CONTENTS
’S DECLARATION STUDENT ...... ii
ACKNOWLEDGEMENT ...... iii
DEDICATION ...... iv
ABSTRACT ...... v
TABLE OF CONTENTS ...... vi
LIST OF TABLES ...... ix
LIST OF FIGURES ...... x
CHAPTER ONE ...... 1
1.0 INTRODUCTION ...... 1
1.1 Background of the Study ...... 1
1.2 Statement of the Problem ...... 2
1.3 Purpose of the Study ...... 4
1.4 Research Questions ...... 4
1.5 Importance of the Study ...... 4
1.5.1 Investment Bankers and Advisors ...... 4
1.5.2 Retail and Institutional Investors ...... 4
1.5.3 Policy Makers ...... 4
1.5.4 Researchers and Academicians ...... 5
1.6 Scope of the Study ...... 5
1.7 Definition of Terms ...... 5
1.7.1 Holiday Effect ...... 5
1.7.2 Random Walk ...... 5
1.8 Chapter Summary ...... 6
CHAPTER TWO ...... 7
2.0 LITERATURE REVIEW ...... 7
2.1 Introduction ...... 7
2.2 Preholiday Effects ...... 7
2.2.1 Weekend or Monday Effect ...... 7
2.2.2 Day of the Week Effect ...... 9
2.2.3 January Effect ...... 9
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2.2.4 Holiday Effect ...... 10
2.3 Post holiday Effects ...... 11
2.4 Disparities for Preholiday and Post holiday Effects ...... 11
2.5 Chapter Summary ...... 12
CHAPTER THREE ...... 13
3.0 RESEARCH METHODOLOGY ...... 13
3.1 Introduction ...... 13
3.2 Research Design ...... 13
3.3 Population and Sampling Design ...... 13
3.3.1 Population ...... 13
3.3.2 Sampling Design and Sample Size ...... 13
3.4 Data Collection ...... 14
3.5 Data Analysis ...... 14
3.6 Chapter Summary ...... 16
CHAPTER FOUR ...... 17
4.0 RESULTS AND FINDINGS ...... 17
4.1 Introduction ...... 17
4.2 Description of Data ...... 17
4.2.1 Period of study table ...... 18
4.2.2 NSE 20 share return ...... 20
4.2.3 Daily market index ...... 20
4.3 Pre-holiday effect ...... 21
4.4 Post holiday effects ...... 23
4.5 Individual Holidays Effect ...... 24
4.5.1 New Year Effect ...... 24
4.5.2 Easter Holidays Effect ...... 27
4.5.3 Labour Day Effect ...... 29
4.5.4 Madaraka Day Effect ...... 31
4.5.5 Mashujaa Day Effect ...... 34
4.5.6 Jamhuri (Independence) Day Effect ...... 36
4.5.7 Christmas Holiday Effect ...... 38
4.6 T-test on Abnormal Returns ...... 41
4.7 T-test on Cumulative Abnormal Returns ...... 42 vii
4.8 Chapter Summary ...... 44
CHAPTER FIVE ...... 45
5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS ...... 45
5.1 Introduction ...... 45
5.2 Summary ...... 45
5.3 Discussion ...... 46
5.3.1 Existence of Preholiday effects at the NSE ...... 46
5.3.2 Existence of Post holiday effects at the NSE ...... 48
5.3.3 Disparities between Preholiday and Post Holiday abnormal returns at the NSE .... 48
5.4 Conclusions ...... 49
5.4.1 Existence of Pre-holiday effects at the NSE ...... 49
5.4.2 Existence of Post-holiday effects at the NSE ...... 49
5.4.3 Disparities between Preholiday and Post-holiday abnormal returns at the NSE .... 50
5.5 Recommendations ...... 50
5.5.1 Suggestions for Improvement ...... 50
5.5.2 Suggestions for Further Research ...... 50
REFERENCES ...... 51
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LIST OF TABLES
Table 4.1: Descriptive Statistics ...... 17
Table 4.2: Regression Analysis on Pre-Holiday Effect ...... 21
Table 4.3: Regression Analysis on Post-Holiday Effect ...... 23
Table 4.4: Abnormal and Cumulative Abnormal Returns on New Year ...... 25
Table 4.5: Abnormal and Cumulative Abnormal Returns on Easter ...... 28
Table 4.6: Abnormal and Cumulative Abnormal Returns on Labour Day ...... 30
Table 4.7: Abnormal and Cumulative Abnormal Returns on Madaraka Day ...... 32
Table 4.8: Abnormal and Cumulative Abnormal Returns on Mashujaa Day ...... 35
Table 4.9: Abnormal and Cumulative Abnormal Returns on Jamhuri Day ...... 37
Table 4.10: Abnormal and Cumulative Abnormal Returns on Christmas Day ...... 40
Table 4.11: NSE Abnormal returns ...... 41
Table 4.12: NASI Abnormal returns ...... 42
Table 4.13: NSE Cumulative Abnormal returns ...... 42
Table 4.14: NASI Cumulative Abnormal Returns ...... 43 – Table 4.15: T test on NSE pre and post holiday Abnormal Returns ...... 43 – Table 4.16: T test on NASI pre and post holiday Abnormal Returns ...... 43
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LIST OF FIGURES
Figure 4.1: Normal day NASI returns over five years ...... 18
Figure 4.2: Normal day NSE returns over five years ...... 19
Figure 4.3: Pre-holiday day NASI returns over five years ...... 20
Figure 4.4: Post-holiday day NASI returns over five years ...... 20
Figure 4.5: Post-holiday day NSE returns over five years ...... 21
Figure 4.6: NSE Pre Holiday Returns ...... 22
Figure 4.7: NSE Post Holiday Returns ...... 23
Figure 4.8: New year NSE Abnormal Returns ...... 26
Figure 4.9: New year NASI Abnormal Returns ...... 26
Figure 4.10: Easter NSE Abnormal Returns ...... 29
Figure 4.11: Easter NASI Abnormal Returns ...... 29
Figure 4.12: Labour day NSE Abnormal Returns ...... 31
Figure 4.13: Labour day NASI Abnormal Returns ...... 31
Figure 4.14: Madaraka day NSE Abnormal Returns ...... 33
Figure 4.15: Madaraka day NASI Abnormal Returns ...... 33
Figure 4.16: Mashujaa day NSE Abnormal Returns ...... 34
Figure 4.17: Mashujaa day NASI Abnormal Returns ...... 34
Figure 4.18: Jamhuri day NSE Abnormal Returns ...... 36
Figure 4.19: Jamhuri day NASI Abnormal Returns ...... 36
Figure 4.20: Christmas NSE Abnormal Returns ...... 38
Figure 4.21: Christmas NASI Abnormal Returns ...... 38
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CHAPTER ONE 1.0 INTRODUCTION 1.1 Background of the Study According to Dodd and Gakhovich (2011), the phenomenon of abnormal returns around public holidays has been well documented in developed and emerging markets. Meneu and Pardo (2004) show high abnormal returns on the trading day prior to holidays that are not related to any calendar anomaly and suggests an explanation for the preholiday effect based on the reluctance of small investors to buy on preholidays.
Lin and Liu (2002) explain that people live by the calendar and act accordingly. During the holidays, production either significantly scales down or completely halts, but consumption, and shopping activities surge. As pointed out by Marret and Worthington (2009), a consistent theme in market efficiency literature is on the presence of calendar anomalies or seasonality in stock market returns. Within the literature on calendar anomalies, one of the more well known anomalies is the holiday effect, most characteristically, a pre holiday effect, where abnormally high returns accrue to stock the day before a holiday. The presence of such anomalies as holiday effect in any stock market is the biggest threat to the concept of market efficiency as these anomalies may enable stock market participants beat the market by observing these patterns.
Lucey (2005) postulate that the pre- holiday effect refers to the fact that share returns typically exhibit consistent patterns around holidays, with high and consistent returns on days prior to major holidays. There exists evidence that this anomaly alongide other market anomalies is international but precludes the possibility of the anomaly reflecting the idiosyncratic market characteristics of any one exchange.
Various studies suggest that Nairobi Securities Exchange (NSE) is not fully efficient despite the various reforms and installation of sophisticated information technologies to facilitate trade. Kuria and Riro (2013) found that seasonal anomalies are persistent in the Kenyan market. There are stock variation at the NSE which are caused by various market imperfections, this indicate stock market inefficiency. This inefficiency thus gives a perfect ground for breeding of market anomalies. 1
The history of NSE as documented on its report of 2013. According to the report, NSE is a public market for the trading of securities issued by publically quoted companies and government of Kenya at an agreed price. The NSE is the centre point of Kenya capital market; stocks are listed and traded on the exchange.
The NSE (2013) report underscore that the exchange has been one of the most popular investment markets in Kenya in the recent past due to its high returns. It has become an integral part of the Kenya economy and any fluctuation in this market influences the lives of individuals as well as corporate entities. The NSE deals in both fixed income securities and the variable income securities. It consists of both the primary and secondary market. Currently, there are 65 listed companies grouped into Agricultural, Commercial, Telecommunication, Automobile, Finance and Investments, Manufacturing, Construction and Allied, Energy and petroleum and Growth Enterprise Market Segment.
In China, Mokerjee and Yu (1999) subjects newly established stock markets in Shanghai and Shenzen to efficieny tests and establishes significant negative weekend and positive holiday effects on the stock market returns. Ayodgan and Geoffrey (2003) establish that market returns display a market anomaly in Turkey. Evidence from Czech republic, Slovakia and Slovenia noted by Tonchev and Kim (2004) suggest that stock returns are higher on the trading day before the holiday. Alagidede (2008) subjects the Egypt, Morroco, Kenya, Nigeria, Zimbabwe, South Africa and Tunisia stock markets to efficinecy tests. The study identifies high and significant returns in trading days after a public holiday for South Africa. This finding is however not applicable to the other stock markets in sample namely; Kenya, Egypt, Morocco, Nigeria, Tunisia, South Africa and Zimbabwe.
1.2 Statement of the Problem Efficient market hypotheses (EMH) is explained by Bodie, Kane and Marcus (2009) in light of the argument that since security prices adjust to all new information, the security prices should reflect all the information that is publicly available at any point in time. On the same view, Dodd and Galchovich (2011) observe that there should be no abnormal returns on special occasions such as holidays. 2
Sanaullah, Shah, Ather, Ali and Aslam (2012) acknowledge that there are some factors which may lead to inefficiencies in any capital market as observed in the behavior of investors. The authors point to existence of anomalous stock market patterns that yield to investors and capital owners abnormal returns which disregard the provisions of market efficiency and are against reliability and credibility of financial markets efficiency. The anomalous behavior in financial markets is judged through event studies of; holiday, day of the week, weekend, January, turn of the month, occurrence of unexpected events and intraday effects.
Existence of holiday effect in financial markets is investigated by various scholars leading to diverse findings. Picou (2006) presented existence of an ex-post holiday anomaly as well as existence of an international effect of ex-post holiday reaction to other exchanges. Marrett and Worthington (2007) apply regression analysis to establish that the Australian financial markets have a pre-holiday effect for small capitalization stocks and notes that the sole cause of a strong pre-holiday effect is the market level effect as post holiday effect is non existent in all the markets and industries. Dodd and Gakhovich (2011) investigate the holiday effect in emerging Central and Eastern European (CEE) markets and show that the holiday effect is present in the CEE region, with a number of countries showing abnormal pre-holiday returns and post-holiday returns. The pre-holiday effect is most pronounced in the earlier years of financial market operations, and its importance is declining over time suggesting an improvement in market efficiency in the markets since the opening of stock exchanges. This leads to an inquiry on the effects of market efficiency on the pre and post holiday returns. The findings suggest that New Year and Christmas produce the highest returns and Liquidity before holidays goes down.
Holiday effect at the NSE is investigated by Osman (2004) who posits that holidays effect do not have a significant impact on stock market activity at the NSE. Rasungu (2005) evaluates holiday effects on common shares returns for companies listed at the NSE and does not find existence of the holiday effect. With these conflicting findings in developed markets returns and NSE returns especially on the holiday anomaly, this study revisists the possibile existence of holiday effects at the NSE.
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1.3 Purpose of the Study The main purpose of this study was to investigate the existence of the holiday effect at the Nairobi Securities Exchange (NSE).
1.4 Research Questions This study focused on addressing research questions that included: 1.4.1 Does pre-holiday effect exist at the NSE? – 1.4.2 Does post holiday effect exist at the NSE? – 1.4.3 Are there disparities between the pre and post holiday abnormal returns at the NSE?
1.5 Importance of the Study The study findings are useful to the following constituents:
1.5.1 Investment Bankers and Advisors The findings assist the investment advisors to have enough financial knowledge on how occasions affect investment returns. Since investor choices are influenced by behavioral biases and market efficiency, knowledge on possible return outcomes given occasions help investment advisors to explain to their clientele the informed view of investing and returns.
1.5.2 Retail and Institutional Investors This study is useful to both retail and institutional investors at the Nairobi Securities Exchange. Investors follow closely the day to day performance of the securities market and the findings of this study indicates whether the Nairobi Securities Exchange exhibits holiday effect or not. Confirmation of this finding goes a long way in helping investors to focus on securities which gives them higher returns. For the fund managers who maintain a large portfolio for their clients, the findings of this study helps them in their decision making so that they are more objective in making investment decisions on behalf of their clients.
1.5.3 Policy Makers For the policy makers through the relevant capital market regulation agencies, the study findings would be useful in formulation and implementation of policies and regulations to be able to control and stabilize the performance of stock market which 4
is a signal of the economic stability of the country. This also allows the government to attract, restore and maintain investor confidence in the capital markets of the country.
1.5.4 Researchers and Academicians This study contributes to the scant local literature on the holiday effect and adds to theory in the sense that the findings confirm either the existence or nonexistence of the anomaly and also provides possible explanations for the same. Through this study, academicians would generate more topics for further research in the market anomalies. Academicians can also generate criticism based on the study findings and research models.
1.6 Scope of the Study This study is conducted for four month period in Nairobi, Kenya. It focusses on daily financial market returns for a five year period (2010 to 2014) at the Nairobi Securities exchange with a view of identifying the abnormal returns associated with the gazetted public holidays in Kenya namely; New year, Easter, Labour day, Madaraka day, Mashujaa day, Jamhuri day and Christmas holidays.
1.7 Definition of Terms 1.7.1 Holiday Effect The possibility that stocks exhibit higher returns on average on days preceding holidays. Dodd and Gakhovich (2011) explain that the phenomenon of abnormal returns around public holidays is known as the holiday effect.
1.7.2 Random Walk This is the assumption that price changes from transaction to transaction in an individual security is an independent identically distributed variable and transactions are fairly uniformly spread across time.
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1.8 Chapter Summary This chapter addresses the background of abnormal returns experience before and after holidays in the financial markets. It provides insightful information on market efficiency, abnormal returns and existence of holidays. The chapter articulates the research problem addressed in the study, objective of the study, research questions addressed by the study, justification of the study as well as defining the terms applied in the study. Chapter two highlights the theoretical framework, conceptual framework and a critical review of the literature on the specific objectives. Chapter three discusses the research methodology in relation to the research design, population, sample and sampling techniques employed. The chapter also explains the data collection techniques, data analysis and data presentation techniques as used in the study. Chapter four presents the results and findings from data analysis and presents the descriptive statistics and trends inferred from the data. In Chapter five, a summary of the paper is presented where in the research findings summary, discussions of findings and recommendations in pretext of the finings is availed. The recommendations include the suggested potential research frontiers.
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CHAPTER TWO 2.0 LITERATURE REVIEW
2.1 Introduction This chapter for literature review is examining already existing studies and using them to improve on the study being undertaken. It focuses on the theories and literature and empirical works on financial market anomalies with a focus on the holiday effects. The chapter discusses theories that form basis for the study including random walk hypothesis, efficient market hypothesis and behavioral finance.
2.2 Preholiday Effects The evidence on the pre-holiday effect as explained by Lucey (2005) is that typical index exhibit positive pre-holiday return and the return is not being eroded by an equal or greater post holiday decline. Those returns are also locally derived and are not derived internationally. In Ireland, Lucey (2005) document that on days before unique Irish holidays, there are statistically significant negative returns in a number of stock indices which indicates that holiday effects originate locally. The positive pre- holiday effect characterized by sufficiently positive returns that numerically swamp the negative local influence is established.
2.2.1 Weekend or Monday Effect French (1980) described the weekend effect as the tendency of stock values and prices to be low on Mondays and increase in value and prices on the other days. In his study of the weekend effect he aimed at finding out whether there exists a profiting strategy that could be used in the stock market. He used the calendar time hypothesis and the trading time hypothesis to analyze the daily returns of stocks. The study by French (1980) for the period between 1953 to 1977 established the tendency for returns to be negative on Mondays and be positive on the other days of the week.
In five developed countries namley; US, UK, Japan, Canada and Australia, Jaffe and Westerfield (1985) carried out a study on the stock market returns. They established that the weekend effect is significant in the five countries with negative average Monday returns and high average Friday and Saturday returns for each index. To
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arrive at this conclusion, the researchers computed the returns as a percentage change in the index value from the previous day using the closing prices. This effect entails the difference in return of days in week. The findings have been lowest returns on Monday and exceptionally high return on Friday than other days of week (George and Hess, 1981). Largest variance on Monday and lowest is on Friday. There is mixed findings on it. In European countries of Hongkong and Canada, Dubois and Louvet (1995) established lower returns at the beginning of week though not necessarily on Monday. Agrawal and Kishore (1994) also used a sample of 19 countries to show that there are negative returns on Monday in nine countries and negative returns on Tuesday in eight countries. Also the Tuesday returns are lower than Monday returns in those countries. Negative Monday and positive Friday effects are not observed in Indian market by Raj and Kumari (2006). It was found that Tuesday returns are negative in Indian markets, while the Monday returns were significantly greater than other days. It was because of settlement period in India i.e. 14 days period that starts on Monday and ends at Friday. Agrawal and Kishore (1994) concluded in the findings that weekend effect is present in the half of the countries. While in the other countries the lowest return are on the Tuesday.
In Kenya, Onyuma (2009) sought to determine the existence of daily and monthly seasonal anomalies in the NSE and found out that there exists the day of the week and the January effects. The study applied a regression analysis on price and adjusted – returns data for a twenty seven year period (1980 2006). The study findings infer that first trading day of the week (Monday) has the lowest negative returns, while the last day of the week (Friday) has the largest positive returns when compared to other trading days. The first month of the year (January) also present the largest positive returns when compared to other trading months in the year. From this study, Onyuma – – – (2009) underscore that that the observed day of the week (Monday or Friday) effect and first month of the year (January) on return and volatility enables investors to optimize returns by exploiting knowledge of the regular shifts in the market returns when designing trading styles and strategies. A study by Mokua (2003) in the same discourse establishes that first day of the week (Monday) returns are not significantly lower than other normal week day trading returns. The study therefore revealed that
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there is no weekend effect of stocks traded at the NSE. The study covered a period of five years starting April 1, 1996 to March 31, 2001.
2.2.2 Day of the Week Effect Cabello and Ortiz (2002) demonstrated that there are differences in distributions of stock returns in each of the days-of-the-week. Such that, the average return on Monday (first trading day of the week) is significantly lower than the average return – – during the other normal trading days of the - week. Whereas, according to the Efficient Market Hypothesis (EMH), the expected daily returns on stocks are the same for all days-of-theweek.
Gibbon and Hess (1981) demonstrated the holiday effect using equally weighted portfolios constructed by the centre for research in security prices for the period July 2, 1962 to December 28, 1978 and established that the daily seasonal effect is strong and that there are persistent negative mean returns for stocks and below average returns for bills on Mondays.
Mbuthia (2000) established that Fridays provide significantly higher returns than the other days of the week and that the day of the week effect is significantly affected by the settlement period. Monday returns become the lowest and the negative whereas Fridays mean returns are the highest and positive when the effect of the change in trading system is considered. The study notes January average returns are higher than ’ the non-January months returns. Januarys mean returns are the highest but the ’ coefficient is not significant. Decembers mean returns are second highest and the coefficient is highly significant.
2.2.3 January Effect In the US, Rozeff and Kinney (1976) made the pioneer attempts to document the evidence of higher mean returns in January as compared to other months of the year. Bhardwaj and Brooks (1992) for 1977-1986 and Eleswarapu and Reinganum (1993) for 1961-1990 noted that the effect has been found to be present in other countries as well (Gultekin and Gultekin 1983) the January effect has also been documented for bonds, this is also seconded by Chang and Pinegar (1986).
Keong (2010) concluded that most of the Asian markets exhibit positive December expect Hong Kong, Japan, Korea and china. Few countries showed positive January 9
effect, April effect and May effect as only Indonesia indicated existence of negative August effect. Agrawal and Kishore (1994) observe that January effect is due to tax loss saving at the end of the tax year, portfolio rebalancing and inventory adjustment of different traders and the role of exchange specialist. 2.2.4 Holiday Effect Osman (2004) observe that it is presumed that stocks exhibit higher returns on average on the days preceding holidays. In a study investigating the holiday affect at the NSE, the researcher established that no holiday effect existed on stocks at the NSE. The study covered a period of nine years from January 1998 to December 2006 taking into account the eight-day window, four days before and four days after the holiday. The study used NSE listed companies and applied regression and correlation analysis in analyzing the data.
Chong et al. (2005) investigated existence of pre-holiday effect across three financial – markets for the period 1973 2003. The markets were; U.S, U.K and Hong Kong. S&P 500, FT 30 and Hang Seng indices were used for U.S, U.K and Hong Kong markets respectively. The study finds a strong evidence supporting existence of pre holiday effect in all the three financial markets and the effect is notably more significant for U.K and Hong Kong markets. The study establishes that average returns on days specifically before a holiday was more than average returns on normal trading days or non pre holidays). A further tests was conducted to establish if the pre holiday anomaly persists over the years or has declined over the years in all the three financial markets. Time series regression analysis results point to declining pre holiday effect in the U.S financial markets in the 1990s. The noted decline in the U.S financial markets is not existent in the U.K and Hong Kong financial markets.
Al-Loughani (2005) investigated the holiday effect at the Kuwait stock exchange (KSE) for the period 1984-2000. The stock returns during trading days right before a – holiday (pre holiday) and the rest of the trading days of the year (normal trading days) were compared. The study findings indicate no noticeable difference between the invasion and post invasion periods suggesting non existence of the holiday effect at the KSE. A further analysis investigating particular patterns for returns during the time surrounding holidays established that post holiday returns were higher than pre holidays returns and other normal trading days returns. The reason for this 10
observation was that investors usually engage in selling prior to the holidays and immediately after the holidays, the investors re-construct investment portfolios again.
2.3 Post holiday Effects Kim and Park (1994) provides evidence of holiday effect by reporting abnormally high returns on the trading day before holidays in all three of the major stock markets in the US namely; NYSE, AMEX and NASDAQ. The study finds that the holiday effect is alos present in the UK and Japanese stock markets even though each country has different holidays and institutional arranegements. Further, holiday effects in the different countries stock markets are independent of the holiday effects in the US stock markets.
According to the efficient market hypothesis (EMH) by (Fama 1970), stock prices follow a random walk and past information or patterns cannot be used to predict the future. Dodd and Galchovich (2011) therefore observes that there should be no abnormal returns on special occasions such as holidays, as these holidays are predetermined and contain no relevant information for stock prices. Ang and Bekaert (2007) however observe that evidence against the EMH is growing, and numerous studies have documented return predictability including predictability around post public holidays. All these constitute a wide and an extensive literature developed challenging the assumption of market efficiency.
2.4 Disparities for Preholiday and Post holiday Effects Zafar et al. (2012) explain that holiday effect is all about human behavior before the holidays. It is always observed that before public holidays, investors react very positively and highly participate in trading, therefore returns are usually higher than post holidays. After holidays, investors are psychologically depressed or not in form and so, their returns remain low. This theory helps us to understand how emotions and behaviours influence financial decisions causing investors to behave in unpredictable and irrational manner thereby creating market imperfections that result in anomalies such as holiday effect.
Al-Loughani (2005) investigated the holiday effect at the Kuwait stock exchange (KSE) for the period 1984-2000. The stock returns during trading days right before a 11
– holiday (pre holiday) and the rest of the trading days of the year (normal trading days) were compared. The study findings indicate no noticeable difference between the invasion and post invasion periods suggesting non existence of the holiday effect at the KSE. A further analysis investigating particular patterns for returns during the time surrounding holidays established that post holiday returns were higher than pre holidays returns and other normal trading days returns. The reason for this observation was that investors usually engage in selling prior to the holidays and immediately after the holidays, the investors re-construct investment portfolios again.
2.5 Chapter Summary This chapter provided literature on various financial market anomalies. The first section provided literature review on calendar anomalies in financial markets. The second section examined literature on random walk theory and the holiday effect. The third section explored literature on efficient market hypothesis and holiday effect. The fourth section presented literature on behavioral finance and holiday effect. The following chapter presents the research plan pursued by the study by indicating the study methodology.
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CHAPTER THREE 3.0 RESEARCH METHODOLOGY 3.1 Introduction This chapter indicates the techniques and procedures adopted by the researcher while conducting the study within the guidance of the objectives conceptualized in the first chapter. The sections are structured to include the research design, the study population, sampling and sample size, data collection techniques and procedures, research procedures and data analysis approaches. A summary of the chapter is last.
3.2 Research Design The study adopted an event study methodology. The basic concept is to find the abnormal return attributable to the event being studied by adjusting for the return that stems from the price fluctuation of the market as a whole (Gilson and Black, 1995).
This research used the event study methodology to carry out the analysis of effects of occurrence of a holiday on stock returns. The event study methodology involved defining the event, which in this case is the holiday. The event window is 5 days before and after the holiday.
3.3 Population and Sampling Design 3.3.1 Population Population is defined by Cooper and Schindler (2006) as the total collection of elements about which one wish to make inferences while undertaking a research study. The study adopts a census covering all the firms listed at the NSE. A total of 65 firms were listed at the NSE for year 2010 to 2014.
3.3.2 Sampling Design and Sample Size 3.3.2.1 Sampling Frame The sampling frame is defined by Cooper and Schindler (2006) as an exhaustive list of all the cases in the population of a study from which a sample will be drawn. With probability samples, the chance or probability of each case being selected is known
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and is usually equal for all cases. With this therefore, the sample frame consist of a total of 65 firms that are listed at the NSE for year 2010 to 2014. 3.3.2.2 Sampling Techniques This study will adopt convenience sampling technique. In convenience sampling technique, the subjects are selected because of their convenient accessibility and proximity to the researcher (Cooper and Schindler, 2006). The researcher used purposive sampling for the companies.
3.3.2.3 Sampling Size According to Cooper and Schindler (2006), sample size is essential for cost- effectiveness. Sample size is a relatively smaller set of the whole population. An under - sized research is considered a waste of economic resources for not having the capability to manufacture functional results. An over - sized study uses more economic resources than required. Bearing this in mind, only 50 firms meet the condition of the study that is those firms that were listed throughout the period of the study.
3.4 Data Collection Secondary data was obtained from the NSE database. Data for the five year period 2010 to 2014 was sourced. The data series comprised of the daily market index, daily prices (closing price and opening price), and volume weighted average price (turnover per counter divided by shares traded) of the firms listed at the Nairobi Securities Exchange. The period chosen coincides with the period NSE changed to a company limited by shares and after many reforms had been undertaken and therefore helped in achieving uniformity of data. It also coincides with the implementation of the new constitution which specifies the public holidays in Kenya.
3.5 Data Analysis The study focused on stock market return volatility during holidays at Nairobi Securities Exchange for a period of five years from January 3, 2010 to December 30, 2014. Daily stock market returns were computed for the stocks of all the companies in the population over the period of study as:
Rt = (NSEt- NSEt-1/NSEt-1)* 100
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÷2 Non parametric test ( ) statistic tests was conducted to establish whether the – proportion of positive pre or post holiday returns is significantly different from the proportion of positive returns on normal days. Specifically: ÷ 2 2= 2(O-E) /E Where: O is the actual number of positive returns E is the expected number of days with positive returns
The daily returns were regressed against market index using a regression model as: á â â â Ɛ R(d) = + Pre Holiday + Post Holiday + Non Holiday +
In order to test holiday effect, the study examined the effect of seven public holidays in Kenya, that is 1st January, Easter holidays, Labour day (1st May), Madaraka day
(1st June), Mashujaa day (20th October), Independence day (12th December), Christmas holidays (25th and 26th December).
Abnormal returns around the holiday were computed using a mean-adjusted return approach as described by Brown and Warner (1985). Daily excess returns were measured by the mean-adjusted returns approach, that is, for each day and following the holiday, the abnormal or excess return from the stock index were calculated by the following equation. – ARt = Rt R Where:
ARt: the excess of the expected return for index at time t
Rt: Is the return on index at the time of event t R: Is the average return on the index taken over the interval of 10 days in the estimation window. Cumulative abnormal returns (CARs) were also analyzed over the interval of 10 days in the post-event window. The CAR corresponding to a holiday at time t (j=0) was computed as: Ó CARt = ARt Where:
CARt: Is the cumulative abnormal return at time t
ARt: Is the abnormal return at time t 15
In contrast to event-day abnormal returns, which show the immediate investors' reaction on the holiday, the 10-day CARs provide an indication of the market response to the event 5 days following the holiday.
3.6 Chapter Summary This chapter has highlighted the techniques, approaches and procedures adopted by the researcher in the course of conducting the study with the objective of answering the three research questions that were raised in the introductory chapter. The chapter contains: the research design, the population and sample, the data collection methods, sampling design and sample size, research procedures and data analysis techniques. Chapter four that follow next highlights the key study
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CHAPTER FOUR 4.0 RESULTS AND FINDINGS 4.1 Introduction This chapter presents the analysis of data findings on the existence of the holiday effects at the NSE. It shows the descriptive information on the study variables namely; markets return, normal day returns, preholiday returns and post-holiday returns. It further presents the findings of the regression analysis and a chapter summary.
4.2 Description of Data Descriptive statistics was used to provide insights into the pattern of the trend of the data. The descriptive statistics techniques used in the study including mean, mode and standard deviations, variance, maximum and minimum. Table 4.1: Descriptive Statistics NSE NASI NSE Pre NASI Pre NSE Post NASI Post Normal Normal holiday holiday holiday holiday Valid 1191 1191 33 33 33 33 N Missing 0 0 1158 1158 1158 1158 Mean .0002861 .0005891 .0018012 .0016039 .0024618 .0028852 Std. Deviation .00660654 .00670729 .00490055 .00426060 .0086214 .00761770 Skewness 1.223 -.021 .126 -1.385 2.700 1.929 Std. Error of .071 .071 .409 .409 .409 .409 Skewness Kurtosis 16.687 3.756 .485 2.587 7.939 5.152 Std. Error of .142 .142 .798 .798 .798 .798 Kurtosis
Range .11218 .07307 .02305 .01978 .04197 .03832
From table 4.1, the average NSE 20 share mean return over the period for normal days is 0.0002861 (0.028 percent) with a standard deviation of 0.0066. The returns data is positively skewed at 1.223 with a kurtosis of 16.687. The average NSE 20 share mean return over the period for pre-holidays is 0.0018012 (0.18 percent) with a standard deviation of 0.0049. The returns data is positively skewed at 0.126 with a kurtosis of 0.485. The average NSE 20 share mean return over the period for post- holidays is 0.0024618 (0.24 percent) with a standard deviation of 0.0086. The returns 17
data is positively skewed at 2.7 with a kurtosis of 7.939. The average post-holiday returns are thus greater than the average preholiday returns which are greater than the average normal day returns. Table 4.1 above presents that the average post-holiday NASI returns is 0.0028852 (0.2 percent) and is greater than the average pre-holiday NASI returns at 0.0016039 (0.16 percent). On average, the normal days average returns are 0.0005891 (0.058 percent). The post-holiday NASI returns are positively skewed at 1.929 with a kurtosis of 5.152. The Pre-holiday NASI returns are negatively skewed at -1.385 with a kurtosis of 2.587 and the normal days returns are negatively skewed at -0.021 with a kurtosis of 3.756.
Figures 4.1 and 4.2 below show the trends of the NASI returns and NSE returns over time respectively. From the graphs in figures 4.3 and 4.4, it is evident that the market returns swing from positive to negative over time as there is no consistent predictable trend.
4.2.1 Period of study table NASI Normal Day Returns
.04
.03
.02
.01
.00
-.01
-.02
-.03
-.04 250 500 750 1000 Figure 4.1: Normal day NASI returns over five years
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NSE Normal Day Returns
.08
.06
.04
.02
.00
-.02
-.04 250 500 750 1000
Figure 4.2: Normal day NSE returns over five years
As indicated in figures 4.1 and 4.2 above, the normal days returns swing between positive and negative returns exhibiting that the normal days returns have equal chance of being positive or negative. Predictability of such returns is thus a challenge.
As presented in Figures 4.3 and 4.4, majority of the preholiday returns at the NSE are positive. Figure 4.5 presents that the NASI preholiday returns swing between positive and negative. However, in majority of the instances, the returns are positive as compared to the negative returns instances. Similarly, figure 4.6 shows that the NSE post holiday returns swing between positive and negative and in most of the instances, the returns are positive as compared to the instances of negative returns.
Figures 4.7 and 4.8 further presents that majority of the post holiday returns at the NSE are positive. Figure 4.7 indicates that most of NASI post holiday returns are positive as compared to the negative returns. Figure 4.8 also indicate that most NSE post holiday returns are positive as compared to the negative returns realized.
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4.2.2 NSE 20 share return NASI Pre Holiday Returns
.008
.004
.000
-.004
-.008
-.012
-.016 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
Figure 4.3: Pre-holiday day NASI returns over five years
4.2.3 Daily market index NASI Post Holiday Returns
.04
.03
.02
.01
.00
-.01 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
Figure 4.4: Post-holiday day NASI returns over five years
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NSE Post Holiday
.04
.03
.02
.01
.00
-.01 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32
Figure 4.5: Post-holiday day NSE returns over five years
4.3 Pre-holiday effect
Table 4.2 below presents the preholiday effect on market returns at the Nairobi Securities Exchange. From the table, it is inferred that there is a statistically signficant positive relationship between occurence of a preholiday and market returns at the â Nairobi Securities Exchange ( =0.066, t= 2.202, P<0.05). The model indicates that 0.2% of variations in market return is explained by occurrence of a pre-holiday (Adjusted R Squared = 0.002, P>0.05)
Table 4.2: Regression Analysis on Pre-Holiday Effect
Model Summary Adjusted R Std. Error of Model R R Square Square the Estimate 1 a .063 .004 .002 .00662785
a. Predictors: (Constant), Pre Holiday, Normal Day
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b ANOVA Sum of Model Squares df Mean Square F Sig. 1 Regression .000 2 .000 2.506 .082a
Residual .055 1254 .000 Total .055 1256 a. Predictors: (Constant), Pre Holiday, Normal Day b. Dependent Variable: Market Return
Coefficientsa Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) .001 .001 .657 .512 Normal Day .000 .001 -.010 -.330 .741 Pre Holiday .001 .001 .066 2.202 .028 a. Dependent Variable: Market Return
NSE Pre Holiday Returns
.015
.010
.005
.000
-.005
-.010 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Figure 4.6: NSE Pre Holiday Returns
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4.4 Post holiday effects As presented in table 4.3 below, 0.2% of variations in market returns is explained by variations on the dummy variables representing the post-holiday or normal trading day (Adjusted R Square = 0.002, P>.05). The model infers a statistically significant negative relationship between the market return and the existence of a post-holiday â ( =-0.066, t= -2.202, P<0.05) confirming the existence of a post-holiday effect at the
Nairobi securities exchange. NSE Post Holiday
.04
.03
.02
.01
.00
-.01 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 Figure 4.7: NSE Post Holiday Returns
Table 4.3: Regression Analysis on Post-Holiday Effect
Model Summary Adjusted R Std. Error of Model R R Square Square the Estimate 1 .063a .004 .002 .00662785 a. Predictors: (Constant), Post Holiday, Normal Day
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b ANOVA Sum of Model Squares df Mean Square F Sig. 1 Regression .000 2 .000 2.506 .082a
Residual .055 1254 .000 Total .055 1256 a. Predictors: (Constant), Post Holiday, Normal Day b. Dependent Variable: Market Return
Coefficientsa Unstandardized Standardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) .004 .002 2.042 .041 Normal Day .000 .001 -.010 -.330 .741 Post Holiday -.001 .001 -.066 -2.202 .028 a. Dependent Variable: Market Return
4.5 Individual Holidays Effect The study examined the Pre and Post - effects of seven public holidays in Kenya, that is New year (1st january), Easter holidays, Labour day (1st May), Madaraka day (1st June), Mashujaa day (20th October), Independence day (12th December), Christmas holidays (25th and 26th December).
4.5.1 New Year Effect Table 4.4 below provides the abnormal and Cumulative abnormal returns for ten days around the New Year holiday period for the five years.
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Table 4.4: Abnormal and Cumulative Abnormal Returns on New Year NSE ABNORMAL RETURNS NSE CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 2015 Days 2010 2011 2012 2013 2014 2015 -5 -0.00624 -0.00001 -0.00494 0.00377 -0.00879 -5 -0.00624 -0.00001 -0.00494 0.00377 -0.00879 -4 -0.00849 0.00532 -0.00526 -0.00311 -0.00817 -4 -0.01474 0.00531 -0.01020 0.00066 -0.01695 -3 0.00531 -0.00364 0.00040 -0.00325 0.02075 -3 -0.00318 0.00168 -0.00487 -0.00636 0.01259 -2 -0.00581 -0.00293 -0.00301 -0.00575 0.00023 -2 -0.00050 -0.00656 -0.00261 -0.00899 0.02099 -1 0.00238 0.01230 -0.00026 0.00756 -0.00403 -1 -0.00343 0.00938 -0.00326 0.00181 -0.00380 1 0.00079 0.00833 0.00051 -0.00109 -0.00654 1 0.00079 0.01071 0.01281 -0.00135 0.00102 2 -0.00556 -0.00782 -0.00490 0.00167 -0.00520 2 -0.00476 0.00051 -0.00439 0.00058 -0.01174 3 0.00033 0.00411 0.00349 0.00477 0.00479 3 -0.00522 -0.00371 -0.00140 0.00643 -0.00041 4 0.00148 0.00814 -0.00065 0.00224 0.00740 4 0.00181 0.01225 0.00284 0.00700 0.01220 5 0.00295 0.00009 -0.00951 0.00549 0.00032 5 0.00443 0.00823 -0.01016 0.00772 0.00773 NASI ABNORMAL RETURNS NASI CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 2015 Days 2010 2011 2012 2013 2014 2015 -5 -0.01012 -0.00268 -0.00373 0.01314 0.00667 -5 -0.01012 -0.00268 -0.00373 0.01314 0.00667 -4 -0.00485 0.00485 -0.00395 -0.00636 -0.00761 -4 -0.01497 0.00217 -0.00768 0.00679 -0.00093 -3 -0.00423 -0.00018 0.00019 -0.00562 0.01138 -3 -0.00908 0.00467 -0.00376 -0.01197 0.00377 -2 -0.00321 -0.00722 -0.00469 -0.00036 -0.00064 -2 -0.00744 -0.00740 -0.00450 -0.00598 0.01074 -1 -0.00106 0.00115 -0.00427 0.00042 -0.00981 -1 -0.00427 -0.00606 -0.00896 0.00006 -0.01045 1 -0.00183 0.00770 0.00364 0.00216 -0.00627 1 -0.00183 0.00664 0.00480 -0.00211 -0.00585 2 -0.00462 -0.00283 -0.00340 0.00033 -0.00430 2 -0.00645 0.00487 0.00024 0.00249 -0.01057 3 0.00594 0.00306 -0.00062 0.00769 0.00726 3 0.00132 0.00024 -0.00402 0.00801 0.00296 4 0.00664 0.01416 0.00128 -0.00255 0.00254 4 0.01258 0.01723 0.00066 0.00514 0.00980 5 -0.00613 0.00137 0.00317 0.00883 -0.00046 5 0.00051 0.01553 0.00445 0.00628 0.00208
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Figure 4.8: New year NSE Abnormal Returns
Figure 4.9: New year NASI Abnormal Returns As indicated in figures 4.9 and 4.10 above, the pre new year and post new year abnormal returns are not consistent over the years with increases and subsequent declines. However, except in year 2014, in 2011, 2012 and 2013, the levels of abnormal returns one day after the holiday is relatively higher than the last day to the holiday.
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4.5.2 Easter Holidays Effect Table 4.5 below provides the abnormal and Cumulative abnormal returns for ten days around the Easter holiday period for the five years.
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Table 4.5: Abnormal and Cumulative Abnormal Returns on Easter
NSE ABNORMAL RETURNS NSE CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 0.00227 0.00519 -0.0005 -0.0046 0.00383 -5 0.00227 0.00519 -0.0005 -0.0046 0.00383 -4 -0.007 0.00151 -0.0034 -0.0016 -0.0009 -4 -0.0048 0.0067 -0.0039 -0.0063 0.00296 -3 0.00615 -0.0045 0.006 -0.0003 -0.0012 -3 -0.0009 -0.0029 0.00258 -0.002 -0.0021 -2 0.003 -0.0032 0.00238 0.00948 -0.0024 -2 0.00915 -0.0077 0.00838 0.00915 -0.0037 -1 -0.0039 0.00303 -0.0049 0.00059 0.00127 -1 -0.0009 -0.0002 -0.0025 0.01007 -0.0012 1 -0.0003 -0.0002 -0.0036 0.02929 -0.005 1 -0.0042 0.00288 -0.0084 0.02988 -0.0037 2 -0.0015 0.00205 0.007 -0.0079 -0.0016 2 -0.0018 0.0019 0.00345 0.02136 -0.0066 3 0.00015 -0.0087 0.00491 -0.0145 0.00227 3 -0.0013 -0.0066 0.01191 -0.0224 0.00067 4 0.00033 0.00503 -0.0019 -0.0037 0.0051 4 0.00048 -0.0037 0.00301 -0.0182 0.00737 5 0.00085 -0.0003 -0.0061 -0.0067 -0.0013 5 0.00119 0.00475 -0.008 -0.0104 0.00376 NASI ABNORMAL RETURNS NASI CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 -0.0034 0.00622 -0.0015 -0.0041 0.00126 -5 -0.0034 0.00622 -0.0015 -0.0041 0.00126 -4 -0.0082 -0.0036 -0.0048 -0.0006 -0.0023 -4 -0.0116 0.00264 -0.0063 -0.0047 -0.001 -3 0.00757 -0.0023 0.00053 -0.0002 0.0028 -3 -0.0006 -0.0059 -0.0043 -0.0008 0.00052 -2 -0.0015 -0.0041 0.00543 0.0151 0.00234 -2 0.00604 -0.0064 0.00596 0.0149 0.00514 -1 0.00084 0.00014 -0.001 -0.0004 0.00088 -1 -0.0007 -0.004 0.00445 0.01466 0.00322 1 0.00071 -0.003 -0.0052 0.02488 -0.0034 1 0.00154 -0.0028 -0.0061 0.02444 -0.0025 2 -0.0022 0.00302 0.00364 -0.0078 -0.0025 2 -0.0015 6.5E-05 -0.0015 0.01707 -0.0059 3 -0.0019 -0.0034 0.00199 -0.014 0.00589 3 -0.0041 -0.0004 0.00563 -0.0218 0.00337 4 0.0034 0.00291 0.00305 -0.0046 -0.0007 4 0.00151 -0.0005 0.00504 -0.0186 0.00515 5 0.00477 0.00406 -0.0022 -0.0082 -0.0042 5 0.00817 0.00697 0.00086 -0.0128 -0.005
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Figure 4.10: Easter NSE Abnormal Returns
Figure 4.11: Easter NASI Abnormal Returns
As indicated in figures 4.11 above, a day prior to the easter holidays, except for 2011 and 2014, the abnormal returns decline and rises on the day after the holiday. For the years 2011 and 2014, the abnormal returns increases and declines on a day after the holiday. For the NASI, a day prior to the holiday in all the years, the abnormal returns are minimal and swings in subsequent days after the holiday to positive and negative levels.
4.5.3 Labour Day Effect Table 4.6 provides the abnormal and Cumulative abnormal returns for ten days around the Labour day period for the five years. 29
Table 4.6: Abnormal and Cumulative Abnormal Returns on Labour Day
NSE ABNORMAL RETURNS NSE CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 0.001515 0.003167 0.00204 -0.0046 0.002715 -5 0.001515 0.003167 0.00204 -0.0046 0.002715 -4 -0.01196 -1.5E-05 -0.00129 -0.01122 0.005547 -4 -0.01045 0.003152 0.000752 -0.01582 0.008262 -3 -0.01238 0.002186 -0.00707 0.002693 -0.00089 -3 -0.02434 0.002171 -0.00835 -0.00853 0.004656 -2 0.005636 -0.00854 -0.00715 -0.00634 -0.0015 -2 -0.00675 -0.00636 -0.01422 -0.00365 -0.00239 -1 0.006326 0.005163 0.002635 -0.00124 -0.00269 -1 0.011962 -0.00338 -0.00451 -0.00758 -0.00419 1 -0.00555 -0.00014 -0.00237 0.003147 0.001041 1 0.000773 0.005021 0.000263 0.001911 -0.00165 2 0.006662 -0.00051 0.011643 0.005187 -0.00707 2 0.001109 -0.00065 0.009271 0.008335 -0.00603 3 0.003267 -0.00078 0.00645 0.003554 0.001313 3 0.009929 -0.0013 0.018094 0.008741 -0.00576 4 0.003153 0.000443 -0.00411 0.005602 -0.00049 4 0.00642 -0.00034 0.002339 0.009156 0.000825 5 0.003338 -0.00096 -0.00078 0.003216 0.002029 5 0.006491 -0.00052 -0.00489 0.008818 0.001541 NASI ABNORMAL RETURNS NASI CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 -0.00082 -6.2E-05 0.008021 -0.00072 0.007816 -5 -0.00082 -6.2E-05 0.008021 -0.00072 0.007816 -4 -0.00795 -0.00315 0.003701 -0.0118 0.001181 -4 -0.00877 -0.00322 0.011722 -0.01251 0.008998 -3 -0.01302 0.002822 -0.01612 -0.00462 -0.00229 -3 -0.02097 -0.00033 -0.01242 -0.01641 -0.00111 -2 0.012509 -0.00358 -0.00649 -0.00453 0.002632 -2 -0.00051 -0.00075 -0.02261 -0.00915 0.000346 -1 0.004836 0.002708 0.001431 0.001941 -0.00255 -1 0.017344 -0.00087 -0.00506 -0.00259 8.14E-05 1 -0.00115 0.003861 0.000774 0.007918 0.002544 1 0.003688 0.006569 0.002206 0.00986 -6.6E-06 2 0.004055 0.001511 0.010114 0.000662 -0.00387 2 0.002908 0.005372 0.010888 0.00858 -0.00132 3 0.002741 0.002873 0.007037 0.003638 -0.00334 3 0.006797 0.004384 0.01715 0.0043 -0.00721 4 -0.00114 -0.00113 -0.00207 0.003826 2.53E-05 4 0.0016 0.001742 0.00497 0.007464 -0.00332 5 -6.5E-05 -0.00585 -0.0064 0.003682 -0.00215 5 -0.00121 -0.00698 -0.00847 0.007508 -0.00213
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Figure 4.12: Labour day NSE Abnormal Returns
Figure 4.13: Labour day NASI Abnormal Returns
As indicated in figure 4.13 above, the one day post labour day NSE abnormal returns increases in 2013 and 2014 but decreases in the other years. These abnormal returns swing in both positive and negative over the years. Figure 4.14 shows that the NASI abnormal returns swings around the Labour day holidays. In the years 2011, 2013 and 2014, the one day post holiday abnormal returns are greater that the one day preholiday abnormal returns.
4.5.4 Madaraka Day Effect Table 4.7 provides the Abnormal and Cumulative abnormal returns for ten days around Madaraka day period for the five years.
31
Table 4.7: Abnormal and Cumulative Abnormal Returns on Madaraka Day
NSE ABNORMAL RETURNS NSE CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 0.001515 0.003847 -0.00832 -0.00255 -0.00337 -5 0.001515 0.003847 -0.00832 -0.00255 -0.00337 -4 -0.01196 -0.00149 -0.00372 0.003035 -0.00052 -4 4 0.002354 -0.01204 0.00049 -0.00389 -3 -0.01238 0.003637 0.003289 0.00397 0.001209 -3 -0.02434 0.002144 -0.00043 0.007004 0.000687 -2 0.005636 -0.00393 0.00717 0.001849 0.003852 -2 -0.00675 -0.00029 0.010459 0.005819 0.005061 -1 0.006326 -0.00122 -0.00602 0.002242 -0.00093 -1 0.011962 -0.00515 0.001154 0.004092 0.002917 1 -0.00555 0.002129 0.008278 -0.00394 -0.0012 1 0.000773 0.000907 0.002262 -0.0017 -0.00214 2 0.006662 -0.00229 -0.004 -0.00023 -0.00025 2 0.001109 -0.00016 0.004279 -0.00417 -0.00146 3 0.003267 -0.00069 0.000485 -0.00017 -0.00249 3 0.009929 -0.00298 -0.00351 -0.0004 -0.00274 4 0.003153 -0.00105 0.005297 0.001008 0.004381 4 0.00642 -0.00175 0.005783 0.00084 0.001892 5 0.003338 0.001066 -0.00246 -0.00522 -0.00067 5 0.006491 1.52E-05 0.002834 -0.00422 0.003714 NASI ABNORMAL RETURNS NASI CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 -0.00082 0.001006 -0.00664 -0.00449 -0.00248 -5 -0.00082 0.001006 -0.00664 -0.00449 -0.00248 -4 -0.00795 -6.7E-05 0.002132 0.00459 -0.00102 -4 -0.00877 0.000939 -0.00451 9.6E-05 -0.0035 -3 -0.01302 -0.00114 -0.00234 -0.00174 -0.00035 -3 -0.02097 -0.00121 -0.0002 0.002852 -0.00136 -2 0.012509 -0.00178 -0.00042 0.002527 0.004614 -2 -0.00051 -0.00292 -0.00276 0.000789 0.004267 -1 0.004836 0.000363 0.004438 0.001181 0.002525 -1 0.017344 -0.00142 0.004014 0.003708 0.007139 1 -0.00115 0.004976 -0.00323 0.000629 -0.00707 1 0.003688 0.005338 0.001213 0.00181 -0.00454 2 0.004055 -0.00114 -0.00042 -0.00048 0.000927 2 0.002908 0.003841 -0.00365 0.000154 -0.00614 3 0.002741 -0.00445 0.000599 0.00055 0.000457 3 0.006797 -0.00558 0.000175 7.5E-05 0.001385 4 -0.00114 0.002078 0.005715 -0.00024 0.00039 4 0.0016 -0.00237 0.006314 0.000311 0.000848 5 -6.5E-05 0.000147 0.000164 -0.00253 0.001997 5 -0.00121 0.002225 0.005879 -0.00277 0.002388
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Figure 4.14: Madaraka day NSE Abnormal Returns
Figure 4.15: Madaraka day NASI Abnormal Returns
As indicated in figure 4.15 above, in 2011 and 2012, the one day post Madaraka day NSE abnormal returns are higher than the preholiday Madaraka day abnormal returns. In 2014, the NSE abnormal returns across the holidays are similar. And in 2010 and 2013, the Pre Madaraka day NSE abnormal returns are more than the post holiday abnormal returns. The returns however fluctuate between the positive and the negative over the period. Figure 4.16 above shows the NASI abnormal returns trend during the Madaraka day holiday period. The one day post Madaraka day NASI abnormal returns for 2011 increases as compared to the pre Madaraka day NASI abnormal returns. For 2010, 2012, 2013 and 2014, the one day post Madaraka day NASI abnormal returns decline as compared to the pre Madaraka day NASI abnormal returns. 33
4.5.5 Mashujaa Day Effect As presented in figure 4.17 below, in 2011 and 2012, post Mashujaa day NSE abnormal returns are greater than the pre Mashujaa day NSE abnormal returns. In 2010, 2013 and 2014, the post Mashujaa day NSE abnormal returns are decline when compared to the Pre Mashujaa day NSE abnormal returns.
Figure 4.16: Mashujaa day NSE Abnormal Returns
Figure 4.17: Mashujaa day NASI Abnormal Returns
Figure 4.18 above shows that the 2011, 2012 and 2014 post mashujaa day NASI abnormal returns exceed the pre Mashujaa day NASI abnormal returns. However, in 2013, and 2010, the Pre mashujaa day NASI abnormal returns exceed the post mashujaa day NASI abnormal returns. The abnormal returns however swing between the positive and the negative over the years. Table 4.8 below provides the abnormal and Cumulative abnormal returns for ten days around Mashujaa day period for the five years. 34
Table 4.8: Abnormal and Cumulative Abnormal Returns on Mashujaa Day
NSE ABNORMAL RETURNS NSE CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 -0.00657 -0.00695 0.005224 -0.00096 0.007852 -5 -0.00657 -0.00695 0.005224 -0.00096 0.007852 -4 -0.00108 -0.00635 -0.00268 0.001427 -0.00027 -4 -0.00765 -0.0133 0.002541 0.000464 0.007582 -3 0.003649 0.001387 -0.00796 -0.00237 0.0031 -3 0.002567 -0.00496 -0.01064 -0.00094 0.00283 -2 0.000544 0.00127 -0.00103 -0.00109 -0.00077 -2 0.004193 0.002657 -0.009 -0.00346 0.002333 -1 0.003922 -0.00249 -0.00079 0.005798 8.72E-05 -1 0.004465 -0.00122 -0.00182 0.004704 -0.00068 1 0.003236 -0.00109 0.001484 -0.00122 -0.0019 1 0.007157 -0.00358 0.000694 0.00458 -0.00182 2 0.002847 -0.00214 0.001211 0.001805 0.000479 2 0.006083 -0.00323 0.002695 0.000587 -0.00142 3 -0.00271 -0.00101 0.002184 -0.00107 0.003605 3 0.000136 -0.00314 0.003395 0.000739 0.004084 4 0.007318 0.003622 0.002514 -0.003 -0.00973 4 0.004607 0.002615 0.004699 -0.00406 -0.00612 5 -0.01115 0.013745 -0.00015 0.000674 -0.00246 5 -0.00383 0.017366 0.002365 -0.00232 -0.01218 NASI ABNORMAL RETURNS NASI CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 -0.0041 -0.01381 -0.00184 -0.00338 -0.00129 -5 -0.0041 -0.01381 -0.00184 -0.00338 -0.00129 -4 -7.3E-05 -0.002 -0.00241 -0.00226 0.000984 -4 -0.00417 -0.01581 -0.00425 -0.00564 -0.0003 -3 0.00222 0.002965 -0.00355 0.000634 0.00327 -3 0.002147 0.000965 -0.00596 -0.00162 0.004253 -2 0.0025 0.002944 0.005195 -0.00066 0.000551 -2 0.00472 0.005909 0.001646 -3.1E-05 0.00382 -1 0.004621 -0.00359 -0.00074 0.005838 -0.0049 -1 0.007121 -0.00064 0.004456 0.005173 -0.00435 1 0.000657 0.001773 0.000373 -0.00134 -0.00127 1 0.005278 -0.00181 -0.00037 0.004493 -0.00617 2 0.004371 -0.00185 -0.00087 0.004908 0.006972 2 0.005027 -7.7E-05 -0.0005 0.003563 0.005699 3 -0.00043 0.001619 0.001679 0.000863 0.003837 3 0.003937 -0.00023 0.000805 0.005772 0.010809 4 0.00282 0.005355 -0.00012 -0.00294 -0.00761 4 0.002387 0.006974 0.001554 -0.00207 -0.00377 5 -0.01258 0.006595 0.002289 -0.00165 -0.00055 5 -0.00976 0.01195 0.002165 -0.00459 -0.00815
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4.5.6 Jamhuri (Independence) Day Effect Figure 4.19 below shows that the 2013 post holiday NSE abnormal returns exceed the preholiday NSE abnormal returns. Figure 4.20 below also shows that the NASI post holiday abnormal returns for 2010, 2012, 2013 and 2014 are greater than the NSE and NASI pre holiday abnormal returns.
Figure 4.18: Jamhuri day NSE Abnormal Returns
Figure 4.19: Jamhuri day NASI Abnormal Returns
Table 4.9 below provides the abnormal and Cumulative abnormal returns for ten days around Jamhuri day period for the five years.
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Table 4.9: Abnormal and Cumulative Abnormal Returns on Jamhuri Day
NSE ABNORMAL RETURNS NSE CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 -0.00142 -0.01011 -0.00143 -0.00357 0.008035 -5 -0.00142 -0.01011 -0.00143 -0.00357 0.008035 -4 0.000885 -0.00089 -0.01023 -0.00186 0.006869 -4 -0.00054 -0.011 -0.01166 -0.00543 0.014904 -3 0.001092 0.002417 0.002147 0.002371 0.001238 -3 0.001977 0.001529 -0.00809 0.000509 0.008108 -2 -0.00171 0.009761 -0.00809 -0.0013 -0.00218 -2 -0.00062 0.012179 -0.00595 0.001071 -0.00094 -1 0.003083 0.001825 0.003233 -0.00222 0.002951 -1 0.001372 0.011586 -0.00486 -0.00352 0.00077 1 0.000658 -0.00184 -0.00167 0.003626 0.001588 1 0.003741 -1.1E-05 0.001559 0.001407 0.004539 2 4.9E-05 0.003061 0.004403 -0.00352 0.002116 2 0.000707 0.001226 0.002728 0.000102 0.003704 3 -0.00276 -0.00471 0.00105 -0.00035 -0.00334 3 -0.00271 -0.00165 0.005453 -0.00387 -0.00122 4 -0.00921 0.003638 0.002877 0.002591 -0.01284 4 -0.01197 -0.00107 0.003927 0.002244 -0.01618 5 0.009338 -0.00316 0.00772 0.004231 -0.00444 5 0.000131 0.00048 0.010596 0.006822 -0.01728 NASI ABNORMAL RETURNS NASI CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 -9.3E-05 -0.0041 -0.00327 -0.00832 0.009932 -5 -9.3E-05 -0.0041 -0.00327 -0.00832 0.009932 -4 0.000626 -0.00319 -0.00567 -0.00315 0.012199 -4 0.000534 -0.0073 -0.00894 -0.01146 0.022131 -3 0.003094 -0.00243 0.002733 0.002115 0.001834 -3 0.00372 -0.00563 -0.00293 -0.00103 0.014033 -2 0.003705 0.003093 -0.00284 -0.00612 -0.00166 -2 0.006799 0.000661 -0.0001 -0.004 0.000175 -1 -0.00162 0.006286 0.001626 0.000427 -0.00649 -1 0.002081 0.009379 -0.00121 -0.00569 -0.00815 1 -0.0005 -0.00075 0.004542 0.002714 0.001305 1 -0.00212 0.005538 0.006168 0.003141 -0.00518 2 -0.00399 0.001376 0.000513 -0.00478 0.003556 2 -0.0045 0.000627 0.005055 -0.00207 0.004861 3 -0.00361 -0.00241 -0.00025 0.006308 -0.00686 3 -0.0076 -0.00104 0.000267 0.001525 -0.0033 4 0.002905 0.000918 7.31E-05 0.004341 -0.01108 4 -0.0007 -0.0015 -0.00017 0.010649 -0.01794 5 -0.00051 0.001217 0.002532 0.00646 -0.00274 5 0.002392 0.002135 0.002605 0.010801 -0.01382
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4.5.7 Christmas Holiday Effect As indicated in figure 4.21 and 4.22 below, in 2011, 2012 and 2014, the post christmas NSE abnormal returns is more than the pre christmas NSE abnormal returns. In 2010 and 2014, the pre christmas NSE abnormal returns is more than the post christmas NSE abnormal returns. The pre christmas NASI abnormal returns for 2010 and 2013 are greater than the post holiday abnormal returns. The 2014, 2012 and 2011 post christmas NSE abnormal returns are greater than the preholiday abnormal returns.
Figure 4.20: Christmas NSE Abnormal Returns
Figure 4.21: Christmas NASI Abnormal Returns
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Table 4.10 below provides the abnormal and Cumulative abnormal returns for ten days around Jamhuri day period for the five years.
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Table 4.10: Abnormal and Cumulative Abnormal Returns on Christmas Day
NSE ABNORMAL RETURNS NSE CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 0.006428 -0.00591 0.0023 0.003769 -0.01963 -5 0.006428 -0.00591 0.00230035 0.003769 -0.01963 -4 -0.01042 -0.00397 0.007143 -0.00311 -0.01123 -4 -0.00399 -0.00988 0.00944343 0.000657 -0.03087 -3 -0.00142 0.003875 -0.00139 -0.00325 -0.00162 -3 -0.01184 -9.7E-05 0.005755693 -0.00636 -0.01286 -2 -0.00178 0.000737 -0.00431 -0.00575 -0.00229 -2 -0.00321 0.004612 -0.005692459 -0.00899 -0.00391 -1 0.003131 -0.00106 -0.00463 0.007563 -0.00167 -1 0.001349 -0.00032 -0.008934852 0.001815 -0.00396 1 -0.00286 0.004273 0.00103 -0.00654 0.027252 1 0.000272 0.003216 -0.003600031 0.001023 0.025583 2 -0.00511 -0.00469 -0.00237 -0.0052 0.006731 2 -0.00797 -0.00041 -0.001342145 -0.01174 0.033983 3 0.008697 -0.00397 0.000374 0.004792 0.002466 3 0.003587 -0.00866 -0.001997711 -0.00041 0.009197 4 -0.00242 0.011253 -0.00045 0.007404 -0.00167 4 0.006273 0.007279 -7.8703E-05 0.012195 0.000797 5 0.005762 -0.00054 0.0023 0.000322 -0.00167 5 0.003339 0.010715 0.001846771 0.007725 -0.00334 NASI ABNORMAL RETURNS NASI CUMULATIVE ABNORMAL RETURNS Days 2010 2011 2012 2013 2014 Days 2010 2011 2012 2013 2014 -5 -0.00252 -0.00112 -0.00101 0.013144 -0.01911 -5 -0.00252 -0.00112 -0.001011984 0.013144 -0.01911 -4 -0.00625 -0.00022 0.001447 -0.00636 -0.01077 -4 -0.00878 -0.00135 0.000434938 0.006789 -0.02987 -3 0.0008 0.008041 -0.00092 -0.00562 0.002216 -3 -0.00545 0.007818 0.000528289 -0.01197 -0.00855 -2 0.001315 0.005086 -0.00167 -0.00036 0.012207 -2 0.002115 0.013127 -0.002586484 -0.00598 0.014422 -1 0.002449 -0.00457 -0.00188 0.000421 -0.00207 -1 0.003765 0.000514 -0.003550083 6E-05 0.010133 1 -0.00459 0.002957 0.002255 -0.00627 0.016908 1 -0.00214 -0.00161 0.000372541 -0.00585 0.014834 2 0.000687 -0.00207 -0.00263 -0.0043 0.004896 2 -0.0039 0.000886 -0.000373848 -0.01057 0.021804 3 0.001304 -0.00911 -0.00221 0.007255 -0.00428 3 0.001991 -0.01118 -0.004835391 0.002958 0.000617 4 0.00233 -0.00074 0.004223 0.002543 -0.00207 4 0.003634 -0.00985 0.002016279 0.009798 -0.00635 5 0.004476 0.001752 0.002391 -0.00046 -0.00207 5 0.006806 0.001013 0.006614396 0.002084 -0.00415
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4.6 T-test on Abnormal Returns Tables 4.11 and 4.12 below presents the descriptive statistics as the number of observations (N), the mean and the standard deviation for the New year, Easter, Labour day, Madaraka day, Mashujaa day, Jamhuri day and Christmas abnormal returns (AR) using both the NSE 20 share index and NASI. As indicated in table 4.5 for the NSE 20 share index abnormal returns (AR), for the new year AR, the standard error of the sample mean is 0.0008, for the Easter AR, the standard error of the sample mean is 0.0008, for the Labour day AR, the standard error of the sample mean is 0.0007, for the Madaraka day AR, the standard error of the sample mean is 0.0006, for the Mashujaa day AR, the standard error of the sample mean is 0.0006, for the Jamhuri day AR, the standard error of the sample mean is 0.0006 and for Christmas AR, the standard error of the sample mean is 0.0009. These sample mean standard errors are relatively small implying that there is a high likelihood that the sample mean is close to the population mean.
Table 4.11: NSE Abnormal returns One-Sample Statistics N Mean Std. Deviation Std. Error Mean New Year 50 .000000 .0059765 .0008452 Easter 50 .000000 .0062016 .0008770 Labour Day 50 .000000 .0051527 .0007287 Madaraka day 50 .000000 .0044665 .0006317 Mashujaa day 50 .000000 .0043956 .0006216 Jamhuri Day 50 .000000 .0048419 .0006847 Christmas 50 -.000067 .0068046 .0009623
As indicated in table 4.6 for the NASI abnormal returns (AR), for the new year AR, the standard error of the sample mean is 0.0008, for the Easter AR, the standard error of the sample mean is 0.0008, for the Labour day AR, the standard error of the sample mean is 0.0007, for the Madaraka day AR, the standard error of the sample mean is 0.0005, for mashujaa day AR, the standard error of the sample mean is 0.0005, for the Jamhuri day AR, the standard error of the sample mean is 0.0006 and for Christmas AR, the standard error of the sample mean is 0.0008. These sample mean standard errors are relatively small implying the likelihood that the sample mean is close to the population mean.
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Table 4.12: NASI Abnormal returns One-Sample Statistics N Mean Std. Deviation Std. Error Mean New Year 50 .000000 .0057248 .0008096 Easter 50 .000000 .0059364 .0008395 Labour Day 50 .000000 .0055819 .0007894 Madaraka day 50 .000000 .0039452 .0005579 Mashujaa day 50 .000000 .0041659 .0005891 Jamhuri Day 50 .000000 .0044819 .0006338
Christmas 50 -.000083 .0058959 .0008338
4.7 T-test on Cumulative Abnormal Returns The descriptive statistics of number of observations (N), the mean and the standard deviation for the New year, Easter, Labour day, Madaraka day, Mashujaa day, Jamhuri day and Christmas cumulative abnormal returns (CAR) using both the NSE 20 share index and NASI are presented in tables 4.13 and 4.14 below.
Table 4.13: NSE Cumulative Abnormal returns One-Sample Statistics N Mean Std. Deviation Std. Error Mean New Year 50 -.000059 .0119905 .0016957 Easter 50 .000270 .0084125 .0011897 Labour Day 50 -.000137 .0076151 .0010769 Madaraka day 50 .000079 .0060286 .0008526 Mashujaa day 50 -.000013 .0056584 .0008002 Jamhuri Day 50 -.000274 .0066820 .0009450 Christmas 50 -.000257 .0100301 .0014185
As indicated in table 4.13 above for NSE 20 share cumulative abnormal returns, New year mean cumulative abnormal returns standard error is 0.0016, Easter mean cumulative abnormal returns standard error is 0.0011, Labour day mean cumulative abnormal returns standard error is 0.0010, Madaraka day mean cumulative abnormal returns standard error is 0.0008, Mashujaa day mean cumulative abnormal returns standard error is 0.0008, Jamhuri day mean cumulative abnormal returns standard error is 0.0009 and Christmas mean cumulative abnormal returns standard error is 0.0014. These standard errors are small implying that the sample mean is close to the population.
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Table 4.14: NASI Cumulative Abnormal Returns One-Sample Statistics N Mean Std. Deviation Std. Error Mean New Year 50 .000061 .0074830 .0010583 Easter 50 .000116 .0080509 .0011386 Labour Day 50 .000216 .0084285 .0011920 Madaraka day 50 .000006 .0053005 .0007496 Mashujaa day 50 .000118 .0055807 .0007892 Jamhuri Day 50 -.000139 .0071072 .0010051
Christmas 50 -.000288 .0088226 .0012477
As indicated in Table 4.15 above, the standard error of the mean new year NASI cumulative abnormal returns is 0.0010, the standard error of the Easter NASI cumulative abnormal returns is 0.0011, the standard error of the mean labour day NASI cumulative abnormal returns is 0.0011, the standard error of the mean madaraka day NASI cumulative abnormal returns is 0.0007, the standard error of the mean mashujaa day NASI cumulative abnormal returns is 0.0007, the standard error of the mean jamhuri day NASI cumulative abnormal returns is 0.0010 and the standard error of the mean christmas NASI cumulative abnormal returns is 0.0012.
– Table 4.15: T test on NSE pre and post holiday Abnormal Returns One-Sample Statistics N Mean Std. Deviation Std. Error Mean NSE preholiday 33 .001801 .0049005 .0008531 NSE postholiday 33 .002462 .0086215 .0015008
– Table 4.16: T test on NASI pre and post holiday Abnormal Returns One-Sample Statistics N Mean Std. Deviation Std. Error Mean NASI preholiday 33 .001604 .0042606 .0007417
NASI postholiday 33 .002885 .0076177 .0013261
As indicated in table 4.15 above, the mean NSE preholiday abnormal return is 0.001801 with a standard deviation of 0.0049005 and a standard error mean of 0.0008532. The mean NSE post holiday abnormal return is 0.002462 with a standard deviation of 0.0086215 and a standard error mean of 0.0015008. These standard error means are small implying that there is no significant discrepacy between the pre holiday and the post holiday returns.
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Table 4.16 above shows that the mean NASI perholiday abnormal return is 0.001604 with a standard deviation of 0.0042606 and a standard error mean of 0.0007417. The mean NASI postholiday abnormal return is 0.002885 with a standard deviation of 0.0076177 and a standard error mean of 0.0013261. These small values of standard errors imply no discrepancies between the pre and post holiday abnormal returns at the NSE.
4.8 Chapter Summary
In this chapter, the study findings are presented. The study finds that the average normal day, preholiday and post holiday NSE 20 share mean return for the period are 0.028%, 0.18% and 0.24% respectively. The average post holiday, pre-holiday and normal day NASI returns are 0.2%, 0.16% and 0.058% respectively. Non parametric tests of the holiday effect establishes that the trading day before the holiday has a higher proportion of positive returns than normal days which evidence a pre-holiday effect. Further, post- holiday effect is evidenced as the proprotion of positive returns a day after the holiday is greater than the normal trading days returns. The regression model infers a statistically significant negative relationship between market returns and the existence of a pre- holiday and a statistically significant positive relationship between occurence of a pre- holiday and market returns at the Nairobi Securities Exchange. Chapter five discusses the findings of the study presented in chapter four above and the conclusions drawn from the findings. It provides recommendations based on these findings too.
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CHAPTER FIVE 5.0 DISCUSSION, CONCLUSIONS AND RECOMMENDATIONS 5.1 Introduction This chapter presents discussions of the key findings of the study as established in chapter four, conclusions drawn based on such findings and recommendations there from. The chapter also recommends areas for further academic research and the limitations of the current study.
5.2 Summary The main purpose of this study was to investigate the existence of the holiday effect at the Nairobi Securities Exchange (NSE) with specific objectives of establishing the: existence of Pre-holiday effect at the NSE, existence of Post-holiday effect at the NSE and disparity – between the pre and post holiday abnormal returns at the NSE. This descriptive study considered seven public holidays in Kenya namely; New year (1st January), Easter holidays, Labour day (1st May), Madaraka day (1st June), Mashujaa day (20th October),
Independence day (12th December) and Christmas holidays (25th and 26th December).
– Data on daily NSE 20 share index and NASI for a five year period (2010 to 2014) was useful in deriving the daily returns at the NSE which was categorized accordingly as normal day returns, Pre holiday returns and Post holiday returns. From the returns descriptive statisics, the average NSE 20 share mean return over the period for normal days is 0.0002861 (0.028 percent), the average NSE 20 share mean return over the period for pre-holidays is 0.0018012 (0.18 percent) and the average NSE 20 share mean return over the period for post-holidays is 0.0024618 (0.24 percent). Generally, the average post-holiday returns are greater than the average preholiday returns which are greater than the average normal day returns.
÷2 Non parametric test ( ) adopted from Ariel (1990) conducted on both the NSE 20 share index and NASI returns establish that the trading day before the holiday has a higher proportion of positive returns than for the normal days. This finding evidence existence of the preholiday effect at the NSE. The study also establishes the post holiday effect at the
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NSE explained by the finding that the proprotion of positive returns a day after the holiday is greater than the normal trading days returns.
Regression analyses results using dummy variables establish a statistically significant negative relationship between the market return and the existence of a post-holiday thereby suggesting the existence of a post-holiday effect at the NSE. The study further establishes a statistically signficant positive relationship between occurence of a pre- holiday and market returns at the Nairobi Securities Exchange confirming the existence of a pre-holiday effect.
T - tests on abnormal returns (AR) and cumulative abnormal returns (CAR) in the ten holiday period establish that the sample mean standard errors are relatively small implying the likelihood that the sample mean is close to the population mean for both the NSE 20 share index returns and the NASI returns. This therefore imply that none of the seven holidays generate abnormal or cumulative abnormal returns than the other holidays.
Further T-tests on abnormal returns amongst the pre and post holiday periods for both NSE and NASI establish relatively small mean standard errors which confirms that the sample means are close to the population and as such, there are no notable discrepancies on the abnormal returns between the days.
5.3 Discussion 5.3.1 Existence of Preholiday effects at the NSE The study findings establishes that most returns earned at the NSE prior to the holiday are positive for both the NSE 20 share and the NASI returns when a comparison is made with the instances of the negative preholiday returns. This study is informed by the proposition of Ndonga (2014) that established positive pre holiday mean returns at the NSE and suggested that there is variation of stock market return volatility attributed to changes in post holiday returns.
Focusing on the individual holiday effects, for the New year holiday, there is no notable consistent trend for the abnormal and cumulative abnormal returns prior to the holiday as the abnormal returns swing between positive returns and negative returns. Similar findings as the new year holiday apply for all the other holidays of the year. Though Ariel 46
(1990) observed that stock markets show high mean returns on the trading days prior to holidays, the current study findings suggest mixed results implying that the differences may be attributable to the levels of market efficiency. – Applying the non parametric tests, the study establishes that the proportion of positive preholiday NSE returns and positive preholiday NASI returns are greater than the normal days proportion which confirms the existence of preholiday effects at the NSE. This finding evidence existence of preholiday effects at the NSE and is consistent with studies establishing preholiday effects in developed financial markets like Kim and Park (1994) who noted preholiday effects in different markets with different institutional arrengements like US, Japan and UK markets. The study but concluded that the holiday effect in the markets are independent of the other markets.
– Similar to Cao et al. (2009), pre holiday effect is established in a regression equation having normal day and preholiday returns as dependent variables and the occurence of the pre holiday as the predictor variables. The study highlights that a small proportions of variations in market returns is explained by existence of the pre holiday. The study establishes a statistically significant positive relationship between the return and the pre holiday occurence which further confirms the existence of a pre holiday effect. This finding is similar to the findings by Dodd and Gakhovich (2011) who attributes the improvement to market efficiency since the opening of stock exchanges.
The foregoing findings from non parametric tests and regression analysis are different from the findings of Alagidede (2008) that established that the Kenyan stock market was amongst the markets without pre-holiday effects in Africa. In the sample, South Africa that was credited to have a preholiday effect was on the strength of its sophisticated systems and levels of development which are comparable to markets in developing economies. Since then, the NSE has made several strides in reforms and efficiency that could affect the levels of development thus explaining the differences in findings. Further, In Australia, Marrett and Worthington (2007) observe the preholiday effect especially for small capitalization firms. Since 2007, the NSE has listed various new firms some of which have smaller capitalization especially amongst the Growth enterprise market segment firms.
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5.3.2 Existence of Post holiday effects at the NSE A majority of stock returns generated at the NSE on the trading days after the holiday are established to be positive for both share indices (NSE 20 share index and NASI) when contrasted with the negative post-holiday returns identified. This finding confirms Ndonga (2014) hypothesis of positive post holiday returns. While, exploring the possible differences on individual post-holiday effects, the study establishes no consistent abnormal and cumulative abnormal return trend after the holiday as the abnormal returns change between positive returns and negative returns over the years of the study.
The non parametric tests establish that the proportion of positive post holidays NSE returns and NASI returns are more than the proportion of the negative post holiday returns thus confirming the existence of post holiday effects. These study findings that evidence existence of post-holiday effects at the NSE are consistent with the works of Dodd and Gakhovich (2011) in New Zealand. This finding informs the improvement in the levels of efficiency at the NSE over time that is explained by improved market microstructure and application of information technology in the trading systems that support price discovery and delivery of the underlying assets.
Multivariate regression analysis adapted from Cao et al. (2009), has existence of a post holiday as a dummy variable with the normal day and preholiday returns as dependent variables and the occurence of the post holiday or normal day as the predictor variables. The model establishes that some negligible extent of variations in market returns is explained by existence of the post holiday. This finding establishes a statistically significant negative relationship between the return and the post holiday occurence which further confirms the existence of a post holiday effect. This finding is similar to the findings by Dodd and Gakhovich (2011) in New Zealand.
5.3.3 Disparities between Preholiday and Post Holiday abnormal returns at the NSE In Pakistan, Zafar et al. (2011) established that Pre-holiday returns are significant than post-holiday returns implying that the stock market is inefficient as characterized by – anomalous returns behavior. In the current study, T test results on the mean abnormal NSE pre and post-holiday returns have a relatively small standard error indicating that the
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mean pre-holiday and post-holiday NSE abnormal returns are closer to the population mean.
The foregoing finding confirms that the Post-holiday and the Pre-holiday abnormal – returns do not have wide disparities. Similarly, further T test results on the mean abnormal NASI pre and post-holiday returns establish a relatively small standard error mean that confirms that the abnormal returns are closer to the population mean and as such there are no notable discrepancies. This finding therefore points to a possibility of improved levels of efficiency for the NSE when compared to the Karachi Stock Exchange (KSE) where the earlier study was conducted.
Though, Ndonga (2014) observed that pre holiday returns are significant and when market opens after a holiday, returns remain lower than pre holiday returns. The notion that the holidays affect the investor’s behavior negatively and make them lazy as expected in behavioural finance is not confirmed by the foregoing findings. This suggests that the market returns are determined by asset specific attributes and are independent of the individual investor biases or investment strategies informed by investor attributes.
5.4 Conclusions 5.4.1 Existence of Pre-holiday effects at the NSE Non parametric tests and regression analyses results confirm the existence of the preholiday effect at the NSE. This imply that one day prior to the holiday, the investor tend to earn abnormal returns on trading at the stock exchange. The returns are positively influenced by the existence of a pre-holiday. The relationship is established as statistically significant.
5.4.2 Existence of Post-holiday effects at the NSE It is observed that the proportion of positive returns on post holidays is greater in the non- parametric test thereby confirming the existence of post-holiday effects at the NSE. Multiple regression analysis results confirm a statistically significant negative relationship between existence of a post-holiday and abnormal returns. This finding imply that the market returns tend to revert to normal after the holidays.
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5.4.3 Disparities between Preholiday and Post-holiday abnormal returns at the NSE To examine the existing disparities between pre-holiday and post-holiday abnormal returns at the NSE, T-tests established that the standard error mean are relatively small for both the NASI abnormal returns and the NSE 20 share index abnormal returns. This finding confirms that though the post-holiday abnormal returns tend to decline to normal days levels, there is no significant discrepancies on the immediate trading day after the holiday. 5.5 Recommendations 5.5.1 Suggestions for Improvement 5.5.1.1 Existence of Pre-holiday effects at the NSE Establishment of pre holiday effects suggest some levels of market efficiency comp[arable to developed markets findings. This could be supported by aligning the trade regulations, rules and policies of the NSE in a manner that promotes price discovery in the markets and facilitates ease of asset allocation and transactions over time.
5.5.1.2 Existence of post-holiday effects at the NSE The study findings of existence of post holiday affects at the NSE confirms the improvement in the levels of efficiency at the NSE. Efforts should be made to improve on this through market based price discovery that are not influenced by
the individual and institutional investors behavior and trading strategies.
5.5.1.3 Disparities between Preholiday and Post-holiday abnormal returns at the
NSE The study findings and literature suggest that the NSE is weak form efficient implying – that market prices and hence returns are determined by trends in asset prices past prices. The NSE should focus on improving efficiency so that market prices and returns should reflect all available private and public information which characterize strong form market efficiency.
5.5.2 Suggestions for Further Research The study suggests that further studies can be undertaken on the effects of various other corporate events on returns at the Nairobi securities exchange. Other than the holidays, there are various other factors that influence investor sentiments and subsequently market
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returns including dividend announcements, profit warning announcements, earnings announcements, rights issues, stock splits and release of financial statements. Further studies can also be modeled on how market microstructure especially rules and regulations and trading procedures affect returns at the securities exchange.
In addition to event study methodology, the study suggests that other approaches be adopted such as the filtered GARCH-EVT approach and the non-parametric methodology for use in the study of the effects of events like elections and terrorism on stock market performance. GARCH-EVT approach enables one to study the event-day effect only, though it is computationally intensive. REFERENCES Agrawal, A., & Tandon, K. (1994). Anomalies or illusions? Evidence from stock markets in eighteen countries. Journal of international Money and Finance, 13(1), 83-106.
Alagidede, P. (2008). Month-of-the-year and pre-holiday seasonality in African stock markets.
Al-Saad, K. (2005). The holiday effect and stock return in the Kuwait Stock Exchange. Global Competitiveness, Saturday.
Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there?. Review of Financial studies, 20(3), 651-707.
Ariel, R. A. (1990). High stock returns before holidays: Existence and evidence on possible causes. The Journal of Finance, 45(5), 1611-1626.
ð Aydo an, K., & Geoffrey Booth, G. (2003). Calendar anomalies in the Turkish foreign exchange markets. Applied Financial Economics, 13(5), 353-360.
Bhardwaj, R. K., & Brooks, L. D. (1992). The January Anomaly: Effects of Low Share ‐ Price, Transaction Costs, and Bid Ask Bias. The Journal of Finance, 47(2), 553- 575.
Bodie, Z., Kane, A. & Marcus, J. (2009). Investment. (8th ed.). McGraw Hill, New York.
51
Brockman, P., & Michayluk, D. (1998). The persistent holiday effect: Additional evidence. Applied Economics Letters, 5(4), 205-209.
Cabello, A., & Ortiz, E. (2003). Day of the week and month of the year anomalies in the í Mexican stock market. Revista Mexicana de Econom a y Finanzas, 2(3), 217-241.
Cao, X. L., Premachandra, I. M., Bhabra, G. S., & Tang, Y. P. (2009). Firm size and the pre-holiday effect in New Zealand. International Research Journal of Finance and Economics, 32, 171-187.
Campbell, J. Y., & Yogo, M. (2006). Efficient tests of stock return predictability. Journal of financial economics, 81(1), 27-60. Chang, E. C., & Pinegar, J. M. (1986). Return seasonality and tax-loss selling in the market for long-term government and corporate bonds. Journal of Financial Economics, 17(2), 391-415.
Dodd, O., & Gakhovich, A. (2011). The holiday effect in Central and Eastern European financial markets. Investment Management and Financial Innovations, 8(4), 29- 35.
Dubois, M., & Louvet, P. (1996). The day-of-the-week effect: The international evidence. Journal of Banking & Finance, 20(9), 1463-1484.
Eleswarapu, V. R., & Reinganum, M. R. (1993). The seasonal behavior of the liquidity premium in asset pricing. Journal of Financial Economics, 34(3), 373-386.
Fama, E. F. (1965). The behavior of stock-market prices. Journal of business, 34-105.
Fama, E. F., Fisher, L., Jensen, M. C., & Roll, R. (1969). The adjustment of stock prices to new information. International economic review, 10(1), 1-21.
Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work*. The journal of Finance, 25(2), 383-417.
Fama, E. F. (1998). Market efficiency, long-term returns, and behavioral finance. Journal of financial economics, 49(3), 283-306.
52
French, K. R. (1980). Stock returns and the weekend effect. Journal of financial economics, 8(1), 55-69.
Frankfurter, G. M., & McGoun, E. G. (2002). Anomalies in finance: What are they and what are they good for?. International Review of Financial Analysis, 10(4), 407- 429.
Gibbons, M. R., & Hess, P. (1981). Day of the week effects and asset returns. Journal of business, 579-596.
Gultekin, N. B. (1983). Stock market returns and inflation: evidence from other countries. Journal of Finance, 49-65. Hubert, F. (2001). Behavioral Finance-Theory and Practical Application. Business Economics, 36(3).
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica: Journal of the Econometric Society, 263-291.
Kim, C. W., & Park, J. (1994). Holiday effects and stock returns: Further evidence. Journal of Financial and Quantitative Analysis, 29(01), 145-157.
Kuria, A. M., & Riro, G. K. (2013). Stock market anomalies: A study of seasonal effects on average returns of Nairobi securities exchange. Research Journal of Finance and Accounting, 4(7), 207-215.
Lakonishok, J., & Smidt, S. (1988). Are seasonal anomalies real? A ninety-year perspective. Review of Financial Studies, 1(4), 403-425.
Lucey, B. M. (2005). Are local or international influences responsible for the pre-holiday behavior of Irish equities?. Applied Financial Economics, 15(6), 381-389.
Marrett, G. J., & Worthington, A. C. (2009). An empirical note on the holiday effect in – the Australian stock market, 1996 2006. Applied Economics Letters,16(17), 1769- 1772.
53
Maxwell, W. (1998). An Evaluation of Contigent claims analysis as applied in the credit risk Modeling , Working Paper George Washington University.
Mbuthia, K.S. (2000). Calendar anomalies in the NSE. Unpublished MBA Project, University of Nairobi.
Meneu, V., & Pardo, A. (2004). Pre-holiday effect, large trades and small investor behaviour. Journal of Empirical Finance, 11(2), 231-246.
Mokua, M. (2003). Weekend effect on stock returns at the NSE. Unpublished MBA Project, University of Nairobi.
Mookerjee, R., & Yu, Q. (1999). An empirical analysis of the equity markets in China. Review of Financial Economics, 8(1), 41-60. Nairobi Securities exchange (NSE), 2013. History of Organization. Retrieved from https:/www.nse.co.ke/about-nse/history-of-organization.html.
Oluoch, N.P. (2014). The holiday effect and Stock market volatility: Evidence from NSE. Unpublished MBA Project, University of Nairobi.
Onyuma, S. O. (2009). Day-of-the-week and month-of-the-year effect on the Kenyan stock market returns. Eastern Africa Social Science Research Review, 25(2), 53- 74.
Osman, A. (2004). Study of the holiday effect at the NSE. Unpublished MBA Project, University of Nairobi.
Pettengill, G. N. (1989). Holiday closings and security returns. Journal of Financial Research, 12(1), 57-67.
Picou, A. (2006). Stock returns behavior during holiday periods: evidence from six countries. Managerial Finance, 32(5), 433-445.
Raj, M., & Kumari, D. (2006). Day-of-the-week and other market anomalies in the Indian stock market. International Journal of Emerging Markets, 1(3), 235-246.
54
Rasungu, N. C. (2005). The existence of the holiday effect in the Nairobi Stock Exchange. Unpublished MBA Project, University of Nairobi.
Rozeff, M. S., & Kinney, W. R. (1976). Capital market seasonality: The case of stock returns. Journal of financial economics, 3(4), 379-402.
Sanaullah, A., Shah, S. M. A., Ather, M., Ali, E., & Asla, M. R. (2012). How Behavioral Aspects Affect Market Efficiency-Evidence from KSE 100 Index. Global Journal of Management And Business Research, 12(10).
Samuelson, P. A. (1965). Proof that properly anticipated prices fluctuate randomly. Industrial management review, 6(2), 41-49.
Tonchev, D., & Kim*, T. H. (2004). Calendar effects in Eastern European financial markets: evidence from the Czech Republic, Slovakia and Slovenia. Applied Financial Economics, 14(14), 1035-1043.
Tversky, A., & Kahneman, D. (1981). The framing of decisions and the psychology of choice. Science, 211(4481), 453-458.
Vidanage, T., & Dayaratna-Banda, O. G. (2012). Does Past Information Help Predict Future Price Movements in Emerging Capital Markets? Evidence from the Colombo Securities Exchange. South Asia Economic Journal, 13(2), 241-264.
Zafar, N., Urooj, S. F., Chughtai, S., & Amjad, S. (2012). Calendar Anomalies: Case of Karachi Stock Exchange. African Journal of Business Management, 6(24), 7261- 7271.
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