Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
THE EFFECT OF GENERAL ELECTIONS ON STOCK PRICES FOR FIRMS LISTED IN THE NAIROBI SECURITIES EXCHANGE KABIRU JAMES D61/72939/2012 A MANAGEMENT RESEARCH PROJECT SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE AWARD OF MASTERS OF BUSINESS ADMINISTRATION (MBA), SCHOOL OF BUSINESS, UNIVERSITY OF NAIROBI NOVEMBER 2014 DECLARATION I declare that this project is my original work and has not been submitted for an award of a degree in any other University for examination/ academic purposes. SIGNATURE …………………………………………DATE …………………………. KABIRU JAMES D61/72939/2012 This Research project has been submitted for examination with my approval as the University Supervisor. SIGNATURE …………………………………………DATE …………………………. DR. J.O. ADUDA ii DEDICATION This research project is dedicated to all my family members. Their support for this course will always remain entrenched in me. iii ACKNOWLEDGEMENT I wish to sincerely appreciate my supervisor Dr J. Aduda for the professional advice and guidance extended while udertaking this research project. Particularly am grateful for the timely and well intended instructions provided throughout the study. I also acknowledge all my lecturers and fellow students for their contributions towards this research. Special thanks to my colleague Esther for ensuring that the momentum to clear the Project was sustained. To my family and friends, kindly accept my appreciation for your great support and for allowing me time to concentrate on the project.May God bless you mightly. iv ABSTRACT The relationship between information flow and asset prices behaviour is a key topic in finance. In this project, the ojective was to investigate the effects of political events on the stock market performance at the Nairobi Securities Exchange (NSE). In particular, the study examined whether political-related events mainly the national general elections and their results contain information regarding the changes in prices and volatility at the NSE.Several theories were applied and used to bring out the issue of information flow and stock price volatility. The study was based on a political variable (general elections) event study methodology to establish the behavior of the NSE performance around the sample period, 1997 to 2013. The population of this study comprised of all the firms trading at the NSE in the above period. As per the records at the Nairobi Securities Exchange, there were 61companies listed by March 2013. The period studied cut across beginning the second election held in Kenya under the multiparty rule in 1997, first regime change of 2002, the turbulent election period of 2007 and a second regime change election event of 2013. The study found that the NSE 20 share index exhibited seasonality over the months from the year 1997 to 2013. The findings concluded that market reaction to elections is highly negative or positive depending on the election being analysed. The information content in the general election is therefore useful for valuing the securities in the markets.These findings have important implications for the optimal strategies of risk-averse stock market investors. v TABLE OF CONTENTS Contents Page DECLARATION ................................................................................................................ ii DEDICATION ................................................................................................................... iii ACKNOWLEDGEMENT ................................................................................................. iv ABSTRACT .........................................................................................................................v TABLE OF CONTENTS ................................................................................................... vi LIST OF TABLES ............................................................................................................. ix LIST OF FIGURES .............................................................................................................x ABBREVIATIONS ........................................................................................................... xi CHAPTER ONE ................................................................................................................1 INTRODUCTION..............................................................................................................1 1.1 Background of the Study ...............................................................................................1 1.1.1 General Elections ........................................................................................................2 1.1.2 Stock Returns ..............................................................................................................3 1.1.3 General Elections and Stock Returns ..........................................................................5 1.1.4 Nairobi Securities Exchange .......................................................................................5 1.2 Research Problem ..........................................................................................................7 1.3 Objective of the Study ...................................................................................................8 1.4 Value of the Study .........................................................................................................8 CHAPTER TWO .............................................................................................................10 LITERATURE REVIEW ...............................................................................................10 2.1 Introduction ..................................................................................................................10 2.2 Theoretical Review ......................................................................................................10 2.2.1 Efficient Market Hypothesis .....................................................................................10 vi 2.2.2 Modern Portfolio Theory ..........................................................................................11 2.2.3 The Random Walk Hypothesis .................................................................................11 2.3 Determinants of Stock Returns ....................................................................................12 2.4 Empirical Literature Review ........................................................................................13 2.6 Summary ......................................................................................................................19 CHAPTER THREE .........................................................................................................21 RESEARCH METHODOLOGY ...................................................................................21 3.1 Introduction .........................................................................................................21 3.2 Research Design ..................................................................................................21 3.3 Population and Sampling Design ........................................................................21 3.4 Data Collection ....................................................................................................22 3.5 Data Analysis ......................................................................................................22 CHAPTER FOUR ............................................................................................................26 DATA ANALYSIS AND INTEPRETATION ...............................................................26 4.1 Introduction .........................................................................................................26 4.2 Descriptive Statistics ...........................................................................................26 4.3 Stock Market Performance and 1997 general election ........................................28 4.4 Stock Market Performance and 2002 General Election ......................................31 4.5 Stock Market Performance and 2007 general election ........................................33 4.6 Stock Market Performance and 2013 general election........................................35 4.7 Tests of Significance ...........................................................................................37 4.8 Summary and Interpretation of the Findings .......................................................40 4.8.1 Summary ...................................................................................................................40 4.8.2 Interpretation .............................................................................................................41 vii 4.8.3 Previous Studies ........................................................................................................41 CHAPTER FIVE .............................................................................................................42 SUMMARY, CONCLUSIONS AND RECOMMENDATIONS .................................42 5.1 Summary .............................................................................................................42 5.2 Conclusions .........................................................................................................43 5.3 Recommendations ...............................................................................................43 5.4 Limitations of the Study ......................................................................................44 5.5 Suggestions for Further Research........................................................................45 REFERENCES .................................................................................................................47 APPENDICES ..................................................................................................................51 APPENDIX 1: LISTED FIRMS AT NAIROBI SECURITIES EXCHANGE .................51 viii LIST OF TABLES Table 4.1: 1997 Abnormal returns and Cumulative abnormal……................................. 29 Table 4.2: Abnormal returns and Cumulative abnormal returns……………………...…32 Table 4.3: Abnormal returns and Cumulative abnormal returns………………………...34 Table 4.4: Abnormal returns and Cumulative abnormal returns………………………...36 Table 4.5: Descriptive Statistics for Abnormal Returns………………………………....38 Table 4.6: T – test on Abnormal Returns…………………………………………..……38 Table 4.7: Descriptive Statistics for Cumulative Abnormal Returns…………………....39 Table 4.8: T – test for Cumulative Abnormal Returns…………………………………..40 ix LIST OF FIGURES Figure 4.1: NSE 20 Share Índex Performance……………...………............................... 27 Figure 4.2: NSE Market Returns………………………………………………………...27 Figure 4.3: NSE 20 share index performance 1997 general elections………………..….28 Figure 4.4: 1997 General elections return, abnormal returns and Cumulative returns……………………………………………………...………….30 Figure 4.5: NSE 20 share index performance 2002 general elections…………………...31 Figure 4.6: 2002 General elections return, abnormal returns and Cumulative returns……………………………………………………………...….33 Figure 4.7: NSE 20 share index performance 2007 general elections…………………...33 Figure 4.8: 2007 General elections return, abnormal returns and Cumulative returns……………………………………………………………...….35 Figure 4.9: NSE 20 share index performance 2013 general elections………………...…35 Figure 4.10: 2013 General elections return, abnormal returns and Cumulative returns……………………………………………………..…………..37 x ABBREVIATIONS ANOVA - Analysis of Variances CAR - Cumulative Abnormal Returns FDI - Foreign Direct Investment ICC - International Criminal Court KRA - Kenya Revenue Authority MBA - Masters of Business Administration MM - Market Model NSE - Nairobi Securities Exchange OECD - Organization for Economic Cooperation and Development PBC - Political Business Cycle SPSS - Statistical Package for Social Sciences UIH - Uncertain Information Hypothesis US - United States USA - United States of America xi CHAPTER ONE INTRODUCTION 1.1 Background of the Study Political risk is one of the crucial factors influencing the operation of a country‟s financial market. It can come in many forms such as a new piece of legislation, coup, an election, or a change in the country‟s regime. The performance of a stock market of an economy is of interest to various parties including investors, capital markets, the stock exchange and government among others. Stock market performance is influenced by a number of factors key among them the activities of governments and the general performance of the economy. Economic activities do affect the performance of the stock markets. Other factors that affect markets performance include availability of other investment assets, change in composition of investors, and markets sentiments among other factors (Mendelson, 1976). The relationship between politics and investor behaviour has been studied in numerous countries and in various contexts. Though, there is little consensus among previous studies and many of these have been concentrated on political events in a single country. Previous research suggests that the political uncertainty around elections creates economic uncertainty, which increases investors risk aversion. Though, the conclusions about how stock prices are affected by political events vary a lot depending on type of event and depending on the country investigated. How stock markets are influenced by various events and how abnormal returns occur are of great interest to both investors and researchers (Lehander and Lönnqvist, 2012). Maning (1989) showed that British Telecom Shares, though not the market as a whole, reacted to opinion polls surrounding the 1987 General elections in the face of the impending nationalization. Niederhoffer, Gibbs and Bullock (1970), Peel and Pope (1993) and Gemmil (1992) examine the stock price behaviours during governmental and/or congressional elections in various developed countries, and they find some inefficiency in share prices around the time of elections, implying a profitable trading 1 rule. They argue that changes in government administration caused by elections tend to affect financial policies or legislation, thereby significantly affecting stock prices. 1.1.1 General Elections The Political Business Cycle (PBC) that was pioneered by Nordhaus (1975) pointed out that within an incumbent‟s term in office there is a predictable pattern of policy, starting with relative austerity in early years and ending with the potlatch right before elections. In the PBC literature, there are two schools of thought that try to explain how the political process induces cycles in stock market performance. On one hand, the “opportunistic” PBC theory argues that the incumbent governments use expansionary policy measures to improve the economic situation just before an upcoming election. The existence of this government cycles, also known as presidential cycles in the case of the USA, would imply that significant and positive returns should be observed in the months preceding an election. However, those policy moves having only transitory effects could be foreseen by investors and, if so, no effect on the stock market should be perceived. On the other hand, the “partisan” PBC theory argues that a “partisan” cycle is detectable in stock market returns because left-wing governments, unlike right–wing governments, focus more on expansionary policies, while the right-wing governments are more worried about the control of the inflation. It implies that differences in the ideological composition of the governments will be reflected in economic policies and, as a consequence, in the stock price behavior. The general election periods are recurring in nature and may affect both the political and investment environment of a given country. Campello, (2007) observed that upcoming general elections may create uncertainty which may affect investors‟ decisions and behavior. Changes in investment behavior are reflected from the activities at the Securities Exchange. Bear markets tend to occur at the end of the term year of the party in office while bull markets occur two or three years after elections. 2 1.1.2 Stock Returns Fisher and Jordan, (1995) posit that investors generally attempt to beat the market by identifying undervalued shares and buying them before their prices rise and look for overvalued shares in order to sell them before their prices fall. This implies that an investor„s judgment of the true worth of the shares may be different from the stock market judgment as seen in the current price of the shares. Theoretical explanations for the pricing of securities according to Fisher and Jordan (1995) and Fama (1970) are fundamental analysis, technical analysis, random walk and the Efficient market Hypothesis ideologies. Mishkin and Eugene, (2002) observed that the performance of the stock market is influenced by a number of factors. They include the activities of governments, policies, political process and the general performance of the economy. Other factors that affect the stock markets performance include availability of other investments assets, change in composition of investors, Economic activities and markets sentiments among others. Semi-strong form efficiency market hypothesis asserts that all publicly available information is fully reflected in security prices. It is not therefore possible for technical analysts to beat the market by exploiting public information. The elections are unique events in that the date of an election is certainly known in advance and only the outcome is uncertain. The outcomes that are anticipated from the market often cause prices to move to the implied direction before the date of elections. If the market is semi-strong efficient, the adjustment of prices to the outcome of elections should occur in a very short period of time and there are no trading strategies adopted to earn abnormal returns. On the other hand, if any systematically abnormal returns can be found around elections and used to beat the market, then this phenomenon of election patterns can be viewed as challenging market efficiency. A market where prices reflect all past, public and private information exhibits the strong form efficiency. In this market even if certain investors have monopolistic access to inside information, they cannot make superior returns (Fama, 1970). 3 Black, (1988) argues that the level of the market is affected by the public's confidence in the market and the breadth of its participation. The market will be higher when participation is broad instead of narrow. When more people are willing to share in the risk of the market, each one bears less risk. This means that the expected return on the market can be lower and the market level higher. That element can be termed as liquidity. It often refers to the breadth of interest in a specific stock. When a stock has a liquid market, investors can buy or sell a relatively large amount in a relatively short time without affecting the price too much. When there is broad public participation in the stock market, the level of the market will be high, and a change in one group's desired holdings won't cause a big change in price; other groups will take up the slack. Such a market will be less volatile than one with narrow participation. Ofek and Richardson (2003) showed that investor enthusiasm for stocks could not be offset by pessimistic investors because of short sale constraints and lock-up periods. Nevertheless, it was investor optimism that drove the prices up. Their view is that speculative bubbles are formed because of unusually high optimism of investors. A high level of social mood influences investor optimism. The peaking of a very high social mood causes investors to make decisions based more on emotion (optimism) than rigorous evaluation. The cognitive evaluation analysis illustrates that stock prices have become too high, yet the emotion of optimism becomes a stronger influence in the decision-making process. The outcome is that investors keep buying stocks and drive prices far above fundamental value. One way the social mood may influence investors is through confirmation by other investors. Irungu, (2012) observed that the information made by election announcements is useful for valuing the securities though the market do not value the information contained in a professional election such as the one in 2002. The average cumulative abnormal returns exhibited a reducing trend in the periods preceding announcement and a slower increase after announcement pointing to market absorption of the information in the long run period after the announcement. 4 1.1.3 General Elections and Stock Returns Bialkowski, et al., (2008) posit that the implications of general elections for investors in stocks are tangible and important. Risk-averse agents require an adequate premium whenever they need to take on additional risks. They observed that typical investors are not fully diversified internationally, and it may occasionally happen that they find that all their wealth is invested in a country with upcoming elections. Therefore, the investigation into whether investors are appropriately compensated for bearing political risk associated with elections is crucial. It turns out that the premium offered for the election risk is rather modest and acceptable only for investors with a relatively low degree of risk aversion. Fama (1965) confirmed that stock prices are correlated with future economic activity in statistical regressions. Confidence in the President may implicitly reflect the underlying economic conditions, which are important in determining stock prices. Booth and Booth (2003) found that the difference between the stock returns in the first half and those in the second half of the Presidential term are economically and statistically significant. Santa-Clara and Valkanov (2003) observed that the market excess return was higher under Democrat than Republican presidencies throughout the period from 1927 to 1998 in the United States of America. This anomaly cannot be explained away by variation in business condition proxies. Additional evidence is provided by Nofsinger (2004), who contends that the stock market is a barometer of public sentiment and its movements can indicate whether incumbents will be re-elected. 1.1.4 Nairobi Securities Exchange The securities exchange has been perceived by many as the backbone for most contemporary economies, serving a critical need of raising capital funds for companies at a reasonably low cost as compared to other sources of finance such as borrowing. The securities exchange serves two critical functions; it provides a critical link between companies that need funds to set up new businesses or to expand their current operations and investors that have excess funds to invest in such companies and it provides a 5 regulated market place for buying and selling of shares at prices determined by supply and demand, not withstanding other macroeconomic fundamentals such as interest and inflation rates (Sunde and Sanderson, 2009). In Kenya, dealing in shares and stocks started in the 1920's when the country was still a British colony. However the market was not formal as there did not exist any rules and regulations to govern stock broking activities. Trading took place on a „gentleman's agreement.‟ Standard commissions were charged with clients being obligated to honor their contractual commitments of making good delivery, and settling relevant costs. At that time, stock broking was a sideline business conducted by accountants, auctioneers, estate agents and lawyers who met to exchange prices over a cup of coffee. Because these firms were engaged in other areas of specialization, the need for association did not arise (http://www.nse.co.ke). In 1951, an Estate Agent by the name of Francis Drummond established the first professional stock broking firm. He also approached the then Finance Minister of Kenya, Sir Ernest Vasey and impressed upon him the idea of setting up a stock exchange in East Africa. The two approached London Stock Exchange officials in July of 1953 and the London officials accepted to recognize the setting up of the Nairobi Stock Exchange as an overseas stock exchange (Muga, 1974). In 1954 the Nairobi Stock Exchange was then constituted as a voluntary association of stockbrokers registered under the Societies Act. Since Africans and Asians were not permitted to trade in securities until after the attainment of independence in 1963, the business of dealing in shares was confined to the resident European community. At the dawn of independence, stock market activity slumped due to uncertainty about the future of independent Kenya (http://www.nse.co.ke). The general elections in Kenya are guided by the constitution which provides for general elections to be held after every five years. For several decades following its independence, the Republic of Kenya was widely regarded as one of the most stable 6 countries in an otherwise volatile region. But as has been observed this reputation began to change following the beginning of a transition to multi-party democracy in the early 1990s. Lusinde (2012) has argued that this situation will not change since a key element in Kenya‟s general elections is the role of personalities in elections. The elections which gave rise to the first independence were held in May 1963, under the supervision of the colonial government. The 1969,1974,1979,1983 and 1988 general elections were held under single party system. The 1992, 1997, 2002, 2007 and 2013 general elections were held under multiparty system after the constitution was amended in 1991 to multiparty system in Kenya, (Common wealth, 2006). 1.2 Research Problem The analysis of political cycles in stock market returns has been mostly conducted in the United States, and therein in the context of presidential elections. Part is generic to the extent that institutional rigidities in the political cycle-mandated terms in office for example impose structure upon market returns. Herbst and Slinkman (1984), examined the period from 1926-1977 and found a 48-month cycle during which returns were higher than average, peaking in November during presidential elections. Huang (1985) used data from 1932-1979 and discovered that stock returns were systematically higher in the last half of a political term than in the first, as did Hensel and Ziemba (1995), though with small and large caps. On this basis, Hensel and Ziemba (1995) suggested that political reelection campaigns create policies that stimulate the economy and are positive for stock returns. The opportunistic Political Business Cycle (PBC) implies that policy-makers systematically aim for a rise in stock prices preceding elections (Vuchelen 2003), while the Uncertain Information Hypothesis (UIH) proposed by Brown et al. (1988, 1993) assumes that investors set prices before an event takes place. In responding to the increased uncertainty, investors set stock prices below their fundamental values. An upward corrective trend in security prices will then follow as the election result becomes 7 more certain. As election-induced uncertainty is reduced, the risk-adjusted expected return should fall and stock prices should rise. However, Mehdian et al. (2008) noted that the greatest degree of uncertainty resolution and thus the highest observed returns should be expected in the time period immediately preceding the election date as this is when media coverage and campaigning are at their peak. The aim of this research proposal is to study the link between general elections and the Nairobi Securities Exchange performance. Kenya presents some interesting peculiarities that deserve special attention. Firstly, the whole issue of pending court cases at the International Criminal Court (ICC). Secondly, the post-election chaos that resulted from the 2007-2008 elections affected nearly all the sectors of the economy. In 2007, Foreign Direct Investment (FDI) was at $729m and dropped by almost 75% to $183m in 2008 after the election violence (KRA, Financial report 2009/2010). Majority of the local studies reviewed analysed the effects of elections on firm‟s performance. The studies gave conflicting results, Menge, et al. (2013); Kithinji and Ngugi (2009) found positive relationship while Irungu (2012) found negative relationship. Further these studies analysed the entire market without looking at how the elections affect the different segments of firms listed at the NSE. This study seeks to answer the following research question. Do the general elections in Kenya affect the market performance and is the effect the same in various segments at the NSE? 1.3 Objective of the Study To investigate whether there is a relationship between the general elections and the market performance at the Nairobi Securities Exchange. 1.4 Value of the Study Most of the studies analysing political cycles have been conducted in the United States (US). The findings of this research will be of importance to academicians, other 8 researchers, investors and the stakeholders at the NSE who include the government as it will emphasize the need for peace during elections. To the investors, the study may enlighten them on the behaviour of securities in anticipation of the related five year cycles following the release of election results in the country. It will offer investors a springboard against which to plan their strategies. It will assist them to make decisions whether to buy, hold or sell securities in order to maximize the returns around the time of this event. More knowledge will be added to the scholars as other interested researchers may refer to this study, probably build new ideas around it and some could even test it‟s consistency in future in order to expand the body of knowledge. Other stakeholders for example firm managers may use the findings of this study as a point of reference for evaluating the external environment. This is handy especially when assessing how anticipated political events might affect the value of their firms. The investment advisors using these findings will be able to advice their clients make investment decisions. The government through the Treasury and Capital Markets Authority could use the findings of this study to guide the formulation of policies governing the operations of the securities exchange. 9 CHAPTER TWO LITERATURE REVIEW 2.1 Introduction This chapter covers the theoretical literature, empirical literature and a summary of the previous works undertaken on the relationship between stock prices and the effect of the general elections with a view to answer the research question. 2.2 Theoretical Review A number of hypotheses have been advanced in the theoretical literature review to explain the relationship between information and stock market performance. This section will therefore review the relationship between information and the stock market performance based on existing theories and academic arguments. 2.2.1 Efficient Market Hypothesis The Efficient Market Hypothesis (EMH), introduced by Markowitz in 1952 and subsequently named by Fama in 1970 assumes that financial markets incorporate all public information and asserts that share prices reflect all relevant information. Correct information is important in forming expectations and allowing investors to correctly process all available information, and where the discount rate is consistent with a normatively acceptable preference specification (Samuelson and Fama, 1965). The EMH‟s concept of informational efficiency has a Zen-like, counter-intuitive flavour to it. The more efficient the market, the more random the sequence of price changes generated by such a market, and the most efficient market of all is one in which price changes are completely random and unpredictable. This is not an accident of nature, but is in fact the direct result of many active market participants attempting to profit from their information. Driven by profit opportunities, an army of investors vigorous pursue even the smallest informational advantages at their disposal and in doing so they incorporate their information into market prices and quickly eliminate the profit opportunities that first motivated their trades (Samuelson, 1965). 10 2.2.2 Modern Portfolio Theory In stock markets, a stock price goes up or down on a daily basis. The return of a stock is a ratio of the closing price to the opening price of the stock on daily basis. When investors buy stocks, they always think about how much the profit they will get in return. In general, if the expected return is high, the risk is also high. Modern portfolio theory by Markowitz explains how investors should select a portfolio and make the highest possible return from a certain level of risk or get the lowest possible risk for a certain level of return. There is a positive relationship between the risk and the expected return of a financial asset. When the risk of an asset increases, so does its expected return. What this means is that if an investor is taking on more risk, he is expected to be compensated for doing so with a higher return. Similarly, if the investor wants to boost the expected return of the investment, he needs to be prepared to take on more risk (Markowitz, 1952). 2.2.3 The Random Walk Hypothesis The importance of the EMH stems primarily from its sharp empirical implications many of which have been tested over the years. Much of the EMH literature before LeRoy (1973) and Lucas (1978) revolved around the random walk hypothesis (RWH) and the martingale model, two statistical descriptions of unforecastable price changes that were initially taken to be implications of the EMH (Fama and Blume, 1966). One of the first tests of the RWH was developed by Cowles and Jones (1937), who compared the frequency of sequences and reversals in historical stock returns, where the former are pairs of consecutive returns with the same sign, and the latter are pairs of consecutive returns with opposite signs. French and Roll (1986) document a related phenomenon: stock return variances over weekends and exchange holidays are considerably lower than return variances over the same number of days when markets are open. This difference suggests that the very act of trading creates volatility, which may well be a symptom of Black‟s (1986) noise traders. 11 2.3 Determinants of Stock Returns Identifying the factors that influence stock returns is a major concern for practice and academic research. The topic is a focus of numerous studies in empirical finance (Korteweg, 2009). Research work which is aimed at determining the factors influencing stock returns of firms provides, a conceptual backdrop necessary to guide the financial manager in financial structure planning and decision in order to increase the shareholders wealth. Prior to 1981, much of the finance literature viewed the present value of dividends to be the principal determinant of the level of stock prices. However, Leroy and Porter (1981) and Shiller (1981) found that, under the assumption of a constant discount factor, stock prices were too volatile to be consistent with movements in future dividends. This conclusion, known as the excess volatility hypothesis, argues that stock prices exhibit too much volatility to be justified by fundamental variables. Literature reviewed, nonetheless, found that stock price movements could not be explained solely by dividend variability as suggested by the present value model with constant discounting (Campbell and Shiller, 1989). Shleifer and Vishny, (2000) studied the stock return policies of over 4000 firms from 33 countries around the world. They found that stock return policies vary across legal regimes in a way that is consistent with the idea that stock return payment is the outcome of effective pressure by minority shareholders to limit agency behaviour. Firms in countries with good legal protection of investors tended to have higher payout ratio compared with firms in countries with weaker legal protection. Olowoniyi A. O and Ojenike J.O, (2012) in their study found that expected growth would significantly improve stock return level of firms in developing countries if appropriate attention is focused on it. Efforts at increasing assets of firms are also expected to raise the level of listed firms. Lower net profit after tax is also expected to reduce level of stock return of listed firm. Similarly leverage of firms is also expected to lower the level of stock return. The finding suggests that attention needs to be paid to improving growth 12 and size of the firms in order to benefit the advantages that could arise from substantial stock return. 2.4 Empirical Literature Review Politics have a significant influence on financial markets. Stock markets respond to new information regarding political decision that may affect domestic and foreign policy. As such, market efficiency requires that stock markets absorb news and political uncertainty. Positive stock returns are expected following the resolution of political uncertainty. In contrast, if the outcome of the political uncertainty does not allow investors to immediately measure the negative impact on the stock market, then the political outcome constitutes an uncertainty inducing surprise (Gemmill, 1992). Just as it is important that networks of transportation, electricity and telecommunications function properly, so is it essential that payments can be transacted, capital can be saved and channelled to the most profitable investment projects and that both households and firms get help in handling financial uncertainty and risk as well as possibilities of spreading consumption over time. Financial markets constitute an important part of the total infrastructure for every society that has passed the stage of largely domestic economies (Vuchelen, 2003). According to Jesen et al, (1996) stock market which is part of the financial markets perform among others the following functions in an economy, (i) raising capital for businesses: the stock exchange provides companies with the facility to raise capital for expansion through selling shares to investing public,(ii) Mobilizing savings for investment: when people draw their savings and invest in shares, it leads to a more rational allocation of resources i.e. by promoting business activity hence benefiting several sectors such as agriculture, commerce and industry resulting in a stronger economic growth and higher productivity levels (iii) Redistribution of wealth: by giving a wide spectrum of people a chance to buy shares and therefore become part owners of profitable enterprises, the stock market helps to reduce large income inequalities. Both 13 casual and professional stock investors through stock price rise and dividends get a chance to share profits of promising businesses. An efficient stock market sector will have the expertise, the institution and the means to prioritise access to capital by competing users so that an economy manages to realise maximum output at least cost. This is what economists refer to as the optimum production level. If an economy does not have efficient financial markets there are always the risks that scarce capital could be channelled to non-productive investments as opposed to productive ones, leading to wastage of economic resources and decline (Lee, 1998). Reduction of search and information costs of transactions at the stock market is essential to facilitating growth of the market. Search explicit costs for example money spent to advertise, the desire to sell or purchase a financial asset, and implicit costs such as the value of time spent in locating counter party. The presence of an organized stock market reduces search and information costs (Fabbozi, 1995). Avenue for flotation of private companies and government owned entities which in turn allow greater growth in case of the supply of assets available for long term investment are available at the stock market. This leads to wealth redistribution from state and private companies to the investing public since they can share in the returns of the privatized entities. The establishment of an efficient market is therefore indispensable for the economy that is keen on using scarce capital resources to achieve economic growth (www.nse.co.ke). Most of the authors who have examined stock returns in relation to both the presidential cycle and election effect agree their findings warrant the view that politics and market returns are correlated. McCallum (1978) studied previous United States administrations and found evidence that stocks showed consistent return patterns dependent upon which year of their four terms they are serving, but did not believe this could be manipulated by the controlling power, contrary to the findings of Nordhaus (1975). Nordhaus (1975) 14 findings suggested governments can affect the state of their economy by influencing the level of unemployment, and may do so strategically to gain re-election. This behaviour is seen to be negatively associated with stock returns as it is inflationary, and therefore it is not unreasonable to assume individuals may wish to diversify their investments among a number of different instruments dependent upon the stage of the election cycle (Anderson et al., 2008 and Nordhaus, 1975). Anderson et al., (2008) found stocks and bonds to be more adversely affected than property in periods of higher inflation, and therefore it may be advantageous for individuals to hedge their investments dependent upon the level of unemployment. Booth and Booth (2003) further discovered that returns differed depending upon the political party which was in power. Their study, as are the majority of studies performed in this area, focussed on the US, and discovered fixed securities had significantly higher returns when the ruling party was republican, where small stock excess returns were higher under democratic administrations. Higher stock returns under democratic president in the (US) is suggested by Cahan et al., (2005) to be a surprising finding, and one that goes against conventional wisdom. As Nordhaus (1975) explained, one would assume a right wing government would be better for business, due to their conservative approach to managing economic cycles. Chan et al., (2005) refer to this apparent contradiction as the presidential puzzle, where real returns, particularly for small stock business, performed better under democratic leadership. Hensel and Ziemba (1995) suggested this may be due to democratic governments enacting policies aimed at benefiting small business. However the differences they found between the returns of the two categories of stock were larger than one would expect. Booth and Booth (2003) found the presidential puzzle to benefit small cap stocks, with no significant difference between the returns of large cap stocks during the terms of both democratic and republican presidents. 15 Santa-Clara and Valkanov (2003) noted that large cap stocks do perform better under democratic presidents, although their performance is not great as that of their smaller counterparts. They found that large cap stocks tended to perform an average of 7% better, where small cap stocks produced returns of around 22%. Anderson et al (2008) noted that the US political system is much more complex than those in other parts of the world, as the ruling party may not be able to pass major laws or reforms if they do not control the senate. Forester and Schmitz (1997) studied the effect US election cycles have on international returns and found some interesting observations around international stock returns. Their study showed that stock returns from eighteen Organization for Economic Cooperation and Development (OECD) countries between the years of 1957 and 1966 appeared to follow a pattern consistent with the US presidential cycle, thus indicating the effect of the political cycle may affect more than just the US economy. In their study of eighteen countries they were able to conclude that US presidential cycles are important when determining international stock market risk premiums. Governments have the ability to affect stock returns through the use of both monetary and fiscal policy (Booth and Booth, 2003; Jensen et al, 1996). However debate still continues as to whether such moves produce more predictable markets or whether this tends to lead to inefficiencies within the market. Jacobsen (1999) found that around 50% of traders tended to act irrationally in 10% of the trades they mad, causing stock prices to exceed their fundamental values around 10% of the time. A number of studies examined the long run presidential effect, investors are assumed to be able to base their investment decisions around the year in which they are in the presidential cycle (Ferri, 2008). Jensen et al., (1996) and Fama and French (1989) found that, by examining the term premium, default premium and dividend yield, both stocks and bonds exhibit a rational in returns, offering no arbitrage opportunities. Furthermore, McCallum (1978) believes any attempt on behalf of a government to influence an economy through monetary or fiscal policy will be anticipated by companies and investors and their effects will therefore be negated. 16 Cahan et al., (2005) contrasts the findings of US studies to their own study of the Newzealand market and found stock returns to be higher under the right of centre national party. This is contrary to findings in the US where stock returns did vary under different governments. Stocks perform better under their left-of-centre democratic party. This finding is not exclusive to Cahan et al., (2005), but was also discovered by Worthington (2006), and Anderson et al., (2008). Nordhaus (1975) and Anderson et al., (2008) argue that markets perform better under a right wing government. This is true in the cases of New Zealand and Australia, and is believed to be due to left wing governments introducing policies that boost employment, of which inflation is a natural consequence. Higher levels of employment lead to higher levels of inflation and is reflected in significantly lower returns (Nordhaus, 1975). Under a national government in New Zealand, and their equivalent in Australia, the iberal party, returns between 1931 and 2006 were 10.18% and 11.95% per annum respectively, where their labour counterparts only managed to produce 6.60% and 4.49% per annum (Cahan et al., 2005). This finding reaffirms those of Anderson et al (2008) and Worthington (2006) that stock performance differs among political parties, and therefore one may wish to base their investment decisions accordingly. Niederhoffer et al. (1970) analyzed the stock market movements in the days and weeks surrounding US presidential elections, the study of the relationships between politics and the stock market has generated much research of interest. Thus, a great number of studies have analyzed several topics such as the influence of economic events on election voting; the relationship of the expected stock return with economic factors; the link between stock markets performance and political election dates, and the explanatory power of political risk in emerging and developed markets. Recently, some studies have shown new empirical evidence that has boosted the interest for this type of financial literature. This is the case of the event study by Pantzalis et al. (2000) that examine stock market behavior around elections on an international scale (33 17 countries) and found that index abnormal returns are generally positive and significant in the 2 weeks prior to the election week. They noted that this abnormal return is strongest for elections with the highest degrees of uncertainty, in particular, in countries with low rankings of political, economic, and press freedom, and elections in which the incumbent loses. Bialkowski et al. (2008) investigated a sample of 27 OECD countries to test whether national elections induce higher stock market volatility. Their empirical findings indicate that investors are still surprised by the ultimate distribution of votes. Stock prices react strongly in response to this surprise and temporarily elevated levels of volatility are observed. Both Santa-Clara and Valkanov (2003), Booth and Booth (2003) have shown that stock market returns are higher during Democratic than during Republican presidencies at the USA. They found that this difference is not explained by business-cycle variables and that it is not concentrated around election dates. Booth & Booth (2003) also noted excess returns under democratic presidents for a small-cap stock portfolio, while large-cap stock excess returns are not significantly different from each other during the 1926–1996 periods. Moreover, US stock excess returns are significantly higher in the last two years than in the first two years of the presidential term. Vuchelen (2003) focuses on the Belgian market and concludes that when a centre-left coalition takes office after an election, stock prices slightly increase, whereas a centreright coalition would push stock prices down. Besides, a coalition made up of left-wing and right-wing parties (without any centre-parties) is said to be perceived as negative by investors. Leblang and Mukherjee (2005) constructed a model of speculative trading to show how government partisanship and trader‟s anticipation of electoral victory by the left-wing or the right-wing party affects the volume of trading and how this, in turn, affects the mean and volatility of stock prices in both the US and the British equity markets. Siokis and Kapopoulos (2007) find that different political regimes impact the conditional variances of the stock market index in Greece, reporting higher volatility increases in the pre-election period and when the right-wing party is in power. 18 Irungu (2012) investigated the informational content of general election results announcenment at the Nairobi securities exchange using events study methodology for the periods 1997 to 2007 and found that the average cumulative abnormal returns exhibited a reducing trend in the periods preceding announcement and a slower increase after announcement pointing to market absorption of the information in the long run period after the announcement. A study by Menge, et al. (2014) on the effect of elections on stock market returns at the Nairobi securities exchange adopting events study methodology covering periods between 2002 and 2013 found that actual stock returns were significantly higher before elections than after election periods. The results led to the conclusion that the expected returns as well as the market returns were significantly higher before elections than after the elections. Their findings concur with the conclusions arrived at by Kithinji and Ngugi (2009). The prices of stocks around the world do not move together in an exact manner. This is because the economic systems in which stock markets are located have dissimilar environments in terms of taxation, industrial growth, political stability, monetary policies and other factors. Stock markets may experience a general increase in level referred to as a bull market or general decrease in price level referred to as bear market. Stagnant prices or sudden big price movement downward is referred to as stock market crash (Kithinji and Ngugi, 2009). 2.6 Summary Though rational partisan theory is persuasive in its focus on electoral uncertainty, it is silent on the issue of policy uncertainty. Alesina (1987) assumes future policies of election winners are fixed and known, but it is much more likely that there is some degree of uncertainty surrounding them. This uncertainty may result from not knowing exactly what economic policies a given party prefers and the inflation that would result if they were implemented. Even though it may be easy to rank-order the impact of Left and Right policies, it may be difficult to know if the victorious parties will implement the moderate or extreme version of its proposals. Uncertainty may also arise because the 19 effectiveness of a government in implementing policy varies, in part due to changing logistical competence and in part due to idiosyncrasies of the current institutional context. A study by Irungu (2012) to investigate the informational content of general election results announcement at the Nairobi Securities Exchange found that the average cumulative abnormal returns exhibited a reducing trend in the periods preceding announcement and a slower increase after announcement pointing to market absorption of the information in the long run period after the announcement. However Kithinji and Ngugi (2009) and Menge, et al. (2014) studying the relationship of elections on stock market returns at the Nairobi Securities Exchange found that actual stock returns were significantly higher before elections than after election periods. Empirical literature reviewed has concluded conflicting results as to the affect of elections on stock prices. This study will investigate whether there is a relationship between the general elections and the market performance and to further analyse if the effect is the same in various segments at the NSE. 20 CHAPTER THREE RESEARCH METHODOLOGY 3.1 Introduction This chapter outlines the research methodology that was used to enable the researcher achieve the research objectives. It outlines the research design, the target population and sampling design, the data collection and the data analysis techniques adopted. 3.2 Research Design Research design is the blue print used to guide a research study to ensure that it addresses the research problem. Research design refers to both structure of the research problem, the framework, organization or configuration of the relationships among variables of a study and the plan of investigation used to obtain the empirical evidence on those relationships (Cooper and Schindler, 2010). 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). The study was based on a political variable (general elections) event study methodology to establish the behavior of the NSE performance around the sample period, 1997 to 2013. 3.3 Population and Sampling Design Population refers to the total collection of elements about which one wishes to make inferences (Cooper and Schindler, 2010). The population of this study comprised all the firms trading at the NSE in the period between 1997 and 2013. As per the records at the Nairobi Securities Exchange, there were 61 (Appendix 1) companies listed by March 2013. However, the sample size of the study was the 20 firms in the NSE share index. The index is a representative of the securities exchange performance. 21 3.4 Data Collection Mugenda and Mugenda (2003) acknowledges there are two types of data; primary and secondary data. The study used secondary data from the Nairobi Securities Exchange and the financial statements of the concerned companies. Data obtained covered the period between 31st December 1997, through 29th December 2002, through 30th December 2007, and 4th March, 2013. 3.5 Data Analysis Data analysis is defined as a process of bringing order, structure and meaning to the data collected so that it can be interpreted and communicated in a research report (Marshall and Rossman, 2006). The collected secondary data was coded and entered into Statistical Package for Social Sciences (SPSS, Version 20.0) for analysis. The study used the following Market Model (MM) steps as outlined by (MacKinlay, 1997); Step 1: Identification of the event of interest The event of interest is the effect of general elections on the return of stocks quoted at the NSE. The dates of elections include 31st December 1997, 29th December, 2002, 30th December 2007 and 4th March 2013. Step 2: Definition of the event window The event window is taken to be 15 days before the election date and 15 days after the election day. The estimation window is 120 days before the event window and the post event window is 60 days after the event window. 22 Estimation Window -120 days Post Event Window Event Window -15 days +15days +45 Days Step 3: Selection of the sample set of firms included in the analysis A sample of 20 firms in the NSE share index was used. Only those companies existing 120 days before election and 60 days after election dates were included. This implies that the figure of 20 has different companies at some points in time. Step 4: Prediction of a “normal” return during the event window in the absence of the event The study first computed the changes recorded in share prices to determine the actual return. 𝐴𝑐𝑡𝑢𝑎𝑙 𝑆ℎ𝑎𝑟𝑒 𝑟𝑒𝑡𝑢𝑟𝑛 𝑖𝑛 𝑑𝑎𝑦 𝑡, = (𝑝 𝑡 −𝑝 𝑡−1 ) 𝑝 𝑡−1 *100 Where; Pt = Price of the security i at time t Pt-1= Price of the security at time t-1 The changes in the NSE index for the same period were also computed. This was denoted as the market return. 𝑁𝑆𝐸 20 𝑠ℎ𝑎𝑟𝑒 𝐼𝑛𝑑𝑒𝑥 𝑅𝑒𝑡𝑢𝑟𝑛 𝑖𝑛 𝑡𝑖𝑚𝑒 𝑡(𝑀𝑎𝑟𝑘𝑒𝑡 𝑅𝑒𝑡𝑢𝑟𝑛) = The following market model was applied; (𝑁𝑆𝐸𝑡 −𝑁𝑆𝐸𝑡−1 ) 𝑁𝑆𝐸𝑡−1 *100 𝐴𝑅 = 𝛼 + 𝛽𝑋 + 𝑒 Where; AR= actual returns X = market return 𝛽 = market risk/partial correlation coefficient for market return and actual returns 23 𝛼 = constant e = error term, The normal or expected returns were generated from the following; 𝑌 = 𝛼 + 𝛽𝑋 Where; 𝑌 = Expected Return Step 5: Estimation of the “abnormal” return within the event window, where the abnormal return is defined as the difference between the actual and predicted returns, without the event occurring The research applied the following model to get abnormal returns. Abnormal Return = Actual Returns -Expected Returns Step 6: Testing whether the abnormal return is statistically different from zero. Test statistics was used to measure the statistical significance of the: a) Cumulative Abnormal Returns (CAR) for each firm b) Cumulative Abnormal Returns (CAR) for all firms c) Cumulative Abnormal Returns (CAR) for all firms - across segments in the NSE. Analysis of variances (ANOVA) was applied for scenario (a) where Cumulative Abnormal Returns (CAR) mean was compared across 4 electioneering periods to check whether some electioneering periods had more informational content compared to others. This approach differs significantly from Menge, et al. (2014) who analysed the CAR for all firms only. A t-test will be applied for Scenario (b) where mean Cumulative Abnormal Returns (CAR) for all firms were aggregated and means of the two periods, that is, before and after election date checked for significant differences. Analysis of Variance was applied for scenario (c) where mean Cumulative Abnormal Returns (CAR) was compared across the various segments. This approach differed significantly from Menge, Mirie and Kimani (2014) who concentrated on scenario (b) only. 24 The level of significance for the ANOVA and t-test was 5% (95 % confidence level). If the significance number found is less than the critical value ( ) set at 0.05, then the conclusion is that the information content of general elections is significant. In other words, there exists significant difference in abnormal returns before and after the general elections. Otherwise the events study concludes that general elections do not influence stock returns. 25 CHAPTER FOUR DATA ANALYSIS AND INTEPRETATION 4.1 Introduction This chapter presents the results of the analysis and findings of the study with reference to the study objectives. The first section gives a summary of the data analysis method used. The second part gives the findings of the study and it includes relevant tables and figures that help to explain the results of the data analysis. The last part gives a summary of findings and interpretation of the results. 4.2 Descriptive Statistics The objective of the study was to establish the effect of a political event specifically the general election on the returns at the Nairobi Securities Exchange (NSE). To achieve this objective, event study methodology was used for four general elections held in Kenya on 31st December 1997, 29th December, 2002, 30th December 2007 and 4th March 2013. The study analyzed the performance of the securities market before and after the general elections. Secondary data obtained from the NSE was compiled and analyzed in Excel format and then transferred to Statistical Package for Social Sciences (SPSS) for further statistical data analysis. The study looked at how the Nairobi security exchange has been performing since the 1997 general election year. The NSE-20 share return was studied for a period of one year before the 1997 general election to one year after the 2013 elections. Figure 4.1 and 4.2 below presents the end of month NSE 20 share index movements and the monthly market returns respectively. As observed from the trendline in figure 4.1, the NSE share index declined steadily from 1997 to 2002 after which it increased substantially in the period between 2002 and 2007. Between 2005 and 2006, the market was volatile ostensibly because of the 2005 referendum. The period after 2007 has been characterised by seasonality in declines and increases in the share index. There is a 26 decline phase between 2011 and 2012 attributed to the post 2010 constitutional referendum. There is observed a steady rise in year 2012. NSE 20 Share Index 6,000 5,000 4,000 3,000 2,000 1,000 1998 2000 2002 2004 2006 2008 2010 2012 Figure 4.1: NSE 20 Share Índex Performance Figure 4.2 shows the volatility and seasonality of monthly NSE market returns as the market returns oscillates between positive and negative values over the months. NSE Percentage Return 20 10 0 -10 -20 -30 1998 2000 2002 2004 2006 2008 Figure 4.2: NSE Market Returns 27 2010 2012 4.3 Stock Market Performance and 1997 general election The study analysed the stock market performance during the 1997 general election. NSE 20 Share Index 3,600 3,500 3,400 3,300 3,200 3,100 3,000 2,900 M6 M7 M8 M9 M10 M11 M12 M1 1997 M2 M3 M4 M5 M6 1998 Figure 4.3: NSE 20 share index performance 1997 general elections Figure 4.3 above, presenting the NSE-20 share index performance trend between mid 1997 and 1998, shows that stock market performance generally declined to November of the same year before rising till beginning of January. The stock performance since then declined sharply till mid 1998. This scenario could depict that bad information content was read from the 1997 election as most investors shied from trading while some sold their portfolio owing to ucertainty of the outcome of the election. 4.3.1 Abnormal Returns (AR) and Cumulative Abnormal Returns (CAR) for 1997 general elections Abnormal returns on the indices 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 event, the abnormal or excess return from the stock index were calculated by the following equation. 28 ARt = Rt – R Where: ARt: Is 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 31 days in the estimation window. Cumulative abnormal returns (CARs) were also analyzed over the interval of 31 days in the post-event window. The CAR corresponding to an event that was happening at time t (j=0) was computed as: CARt =Σ ARt Where: CARt: Is the cumulative abnormal return at time t ARt: Is the abnormal return at time t In contrast to event-day abnormal returns, which show the immediate investors' reaction on the political event (general election), the 30-day CARs provide an indication of the market response to the event 30 days following the general election. Table 4.1: 1997 Abnormal returns and Cumulative abnormal returns Days Share Return Abnormal return Index -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 3049.11 3045.50 3068.72 3077.50 3078.97 3077.97 3063.05 3073.97 3066.67 3069.46 3078.42 Cumulative Abnormal return 0.25152 -0.1184 0.76244 0.28611 0.04777 -0.0325 -0.4847 0.35651 -0.2375 0.09098 0.29191 -0.0608 -0.4308 0.45008 -0.0262 -0.2646 -0.3448 -0.7971 0.04415 -0.5498 -0.2214 -0.0205 29 -0.0608 -0.4916 -0.0415 -0.0678 -0.3323 -0.6772 -1.4743 -1.4301 -1.98 -2.2013 -2.2218 -4 3088.69 -3 3110.85 -2 3113.12 -1 3117.47 1 3118.78 2 3128.01 3 3148.45 4 3188.54 5 3221.56 6 3273.16 7 3301.67 8 3307.07 9 3353.26 10 3344.21 11 3335.18 12 3341.56 13 3338.09 14 3338.49 15 3338.49 Average return 0.33361 0.71746 0.07297 0.13973 0.0419 0.29613 0.65341 1.27335 1.03539 1.60198 0.8709 0.16356 1.39684 -0.27 -0.27 0.19114 -0.1036 0.01189 0 0.31236 0.02125 0.4051 -0.2394 -0.1726 -0.2705 -0.0162 0.34105 0.96099 0.72303 1.28962 0.55854 -0.1488 1.08448 -0.5823 -0.5824 -0.1212 -0.416 -0.3005 -0.3124 -2.2005 -1.7954 -2.0348 -2.2075 -2.4779 -2.4941 -2.1531 -1.1921 -0.4691 0.82055 1.37909 1.23029 2.31477 1.73243 1.15003 1.02881 0.61283 0.31236 0 3 2 Return 1 Abnormal Return 0 -20 -10 0 10 20 -1 Cumulative Abnormal Return -2 -3 Figure 4.4: 1997 General elections return, abnormal returns and Cumulative returns 30 As presented in figure 4.4 above, the normal and the abnormal returns on average move in synchronicity during the event period. The Cumulative abnormal returns decline in the negative before the general election and steadily rises after the event. 4.4 Stock Market Performance and 2002 General Election The study analysed the stock market performance during the 2002 general election. NSE 20 share index 2,200 2,000 1,800 1,600 1,400 1,200 1,000 M6 M7 M8 M9 M10 M11 M12 2002 M1 M2 M3 M4 M5 M6 2003 Figure 4.5: NSE 20 share index performance 2002 general elections Figure 4.5 above shows that the NSE-20 Share Index rose gently but steadily from mid-2002 to 2003. This points to the investors confidence in the then election that led to a regime change in governance in Kenya. 31 4.4.1 Abnormal Returns (AR) and Cumulative Abnormal Returns (CAR) for 2002 general elections Table 4.2: Abnormal returns and Cumulative abnormal returns Days Share Return Abnormal return Index -15 1156.59 -14 1159.71 -13 1162.93 -12 1167.18 -11 1165.92 -10 1178.7 -9 1199.87 -8 1225.95 -7 1242.98 -6 1256.53 -5 1270.95 -4 1270.67 -3 1279.09 -2 1298.5 -1 1298.86 1 1317.45 2 1362.85 3 1384.98 4 1446.75 5 1504.2 6 1538.12 7 1565.84 8 1572.12 9 1578.21 10 1551.06 11 1550.88 12 1509.43 13 1488.59 14 1507.11 15 1518.92 Average return Cumulative Abnormal return -0.39014 0.269759 0.277656 0.365456 -0.10795 1.09613 1.796046 2.173569 1.389127 1.090122 1.147605 -0.02203 0.662643 1.517485 0.027724 1.431255 3.446051 1.623803 4.459992 3.970969 2.255019 1.8022 0.401063 0.387375 -1.7203 -0.0116 -2.67268 -1.38065 1.24413 0.783619 0.910448 -1.30059 -0.64069 -0.63279 -0.54499 -1.0184 0.185682 0.885599 1.263121 0.478679 0.179674 0.237157 -0.93248 -0.24781 0.607037 -0.88272 0.520807 2.535603 0.713355 3.549544 3.060522 1.344571 0.891752 -0.50939 -0.52307 -2.63075 -0.92205 -3.58312 -2.2911 0.333682 -0.12683 32 -1.30059 -1.94128 -2.57407 -3.11906 -4.13746 -3.95178 -3.06618 -1.80306 -1.32438 -1.14471 -0.90755 -1.84003 -2.08783 -1.4808 -2.36352 -1.84271 0.69289 1.406245 4.955789 8.016311 9.360882 10.25263 9.743249 9.220176 6.589425 5.667372 2.084248 -0.20685 0.126829 0.0000000000000018 12 10 8 6 Return 4 Abnormal Return 2 Cumulative Abnormal Return 0 -20 -10 -2 0 10 20 -4 -6 Figure 4.6: 2002 General elections return, abnormal returns and Cumulative returns From figure 4.6 above, it is presented that the cumulative abnormal returns remain negative throughout the pre- event (general election) period and rises after the event. The normal and abnormal returns on average move in tandem in the event period. 4.5 Stock Market Performance and 2007 general election The study analysed the stock market performance during the 2007 general election. NSE 20 Share Index 5,500 5,400 5,300 5,200 5,100 5,000 4,900 4,800 4,700 M6 M7 M8 M9 M10 M11 M12 2007 M1 M2 M3 M4 M5 M6 2008 Figure 4.7: NSE 20 share index performance 2007 general elections 33 Figure 4.7 illustrates that the NSE-20 share performance was erratic, though, receding on average. The decline was more pronounced between the months of December and January 2008. This may be attributed to the events of the post election violence that scared investors at the market. 4.5.1 Abnormal Returns (AR) and Cumulative Abnormal Returns (CAR) for 2007 general elections Table 4.3: Abnormal returns and Cumulative abnormal returns Days Share Return Abnormal return Index -15 5234.54 -14 5205.06 -13 5221.96 -12 5246.16 -11 5266.78 -10 5265.15 -9 5269.08 -8 5332.03 -7 5342.96 -6 5339.80 -5 5278.73 -4 5291.69 -3 5287.93 -2 5339.75 -1 5444.83 1 5167.18 2 5133.48 3 5015.50 4 5180.14 5 5419.93 6 5338.77 7 5341.82 8 5335.23 9 5207.16 10 5124.45 11 5206.15 12 5111.74 13 5098.48 14 5063.44 15 4942.30 Average return Cumulative Abnormal return 0.36777 -0.5633 0.32484 0.46326 0.39307 -0.0308 0.07447 1.19475 0.20511 -0.0592 -1.1437 0.2456 -0.0712 0.98007 1.96784 -5.0994 -0.6522 -2.2983 3.28261 4.6291 -1.4974 0.05712 -0.1234 -2.4004 -1.5885 1.59443 -1.8134 -0.2594 -0.6873 -2.3925 -0.1633 0.531113248 -0.399952826 0.48818222 0.626607461 0.556408142 0.132542642 0.237816966 1.358096393 0.368450019 0.104130726 -0.980356613 0.408939954 0.09216333 1.143411466 2.131186888 -4.936017445 -0.488827413 -2.134936102 3.445947787 4.792444112 -1.334084263 0.22046134 0.03992139 -2.237072917 -1.425121921 1.757773151 -1.650062169 -0.096075893 -0.523915928 -2.229173747 34 0.531113248 0.131160422 0.619342642 1.245950103 1.802358245 1.934900887 2.172717853 3.530814246 3.899264265 4.003394991 3.023038378 3.431978333 3.524141663 4.667553129 6.798740017 1.862722572 1.373895159 -0.761040943 2.684906844 7.477350956 6.143266694 6.363728033 6.403649424 4.166576507 2.741454586 4.499227737 2.849165568 2.753089675 2.229173747 0.000000000 10 6 8 4 6 4 2 0 -20 -10 -2 0 10 20 Return 0 Cumulative Abnormal Return Abnormal Return -2 -4 -4 -6 Figure 4.8: 2 -6 2007 General elections return, abnormal returns and Cumulative returns For the 2007 general elections, the cumulative abnormal return remained positive during the pre – event phase and dipped to the negative on the post event and reverted immediately to the positive and declined thereafter. The normal and abnormal returns moved in uniformity throughout the event period. 4.6 Stock Market Performance and 2013 general election The study analysed the stock market performance during the 2013 general election. NSE 20 Share Index 5,200 5,000 4,800 4,600 4,400 4,200 4,000 3,800 M9 M10 M11 M12 M1 M2 2012 M3 M4 M5 M6 M7 M8 M9 2013 Figure 4.9: NSE 20 share index performance 2013 general elections 35 Figure 4.6 illustrates that the NSE-20 share performance was on an upward trend until after the 2013 general election. There was however a decline in the month after the election and swings in performance in the months thereafter. There was however not a substantial decline indicating that the elections did not affect investor confidence irrespective of the regime change in governamce in Kenya resulting from this election. This was also the first election after enanctment of the new Kenyan constitution. 4.6.1 Abnormal Returns (AR) and Cumulative Abnormal Returns (CAR) for 2013 general elections Table 4.4: Abnormal returns and Cumulative abnormal returns Days Share Return Abnormal return Index -15 -14 -13 -12 -11 -10 -9 -8 -7 -6 -5 -4 -3 -2 -1 1 2 3 4 5 6 7 8 9 10 11 12 4611.03 4633.48 4648.09 4637.54 4614.75 4573.88 4551.06 4502.75 4505.59 4477.89 4463.65 4469.19 4513.55 4518.59 4510.47 4533.82 4546.83 4585.07 4658.64 4796.33 4985.91 4911.45 4831.85 4774.12 4727.04 4721.23 4719.05 Cumulative Abnormal return 0.49276 0.48688 0.31531 -0.227 -0.4914 -0.8856 -0.4989 -1.0615 0.06307 -0.6148 -0.318 0.12411 0.99257 0.11166 -0.1797 0.51768 0.28695 0.84103 1.60456 2.95558 3.95261 -1.4934 -1.6207 -1.1948 -0.9862 -0.1229 -0.0462 0.382910298 0.377024118 0.205461844 -0.336826867 -0.601276249 -0.995490358 -0.60877187 -1.171362861 -0.04677935 -0.724643764 -0.427858835 0.014261757 0.882721682 0.001811852 -0.289553992 0.407832494 0.177102572 0.731173589 1.494703739 2.845731687 3.842753513 -1.603260341 -1.73055456 -1.304632383 -1.096002244 -0.232761813 -0.156026324 36 0.382910298 0.759934416 0.96539626 0.628569392 0.027293143 -0.968197215 -1.576969086 -2.748331947 -2.795111296 -3.51975506 -3.947613895 -3.933352137 -3.050630455 -3.048818604 -3.338372595 -2.930540102 -2.753437529 -2.02226394 -0.527560201 2.318171487 6.160925 4.557664659 2.827110099 1.522477715 0.426475472 0.193713658 0.037687334 13 14 15 4708.56 4713.6 4732.79 -0.2223 0.10704 0.40712 Average Return -0.33214242 -0.294455086 -0.002812822 -0.297267907 0.297267907 0.00000000000000089 0.10985 8 5 6 4 3 4 Return 2 2 1 Cumulative Abnormal Return 0 Abnormal Return 0 -20 -10 0 10 20 -2 -1 -4 -2 -6 -3 Figure 4.10: 2013 General elections return, abnormal returns and Cumulative returns For the 2013 general election, the normal and abnormal returns move in uniform throughout the event window. The cumulative abnormal returns gradually moved to the negative before the event (general election) and subsequently to the positive after the event and subsequently back to the negative. 4.7 Tests of Significance Parametric t-test was used to establish the statistical significance of the abnormal returns (AR) and the cumulative abnormal returns (CAR) over the event window period. 4.7.1 T – test for Abnormal Returns This study tests the following hypothesis: Null Hypothesis: General election has no effect on the Nairobi Securities Exchange Alternate Hypothesis: General election has an effect on the Nairobi Securities Exchange. 37 Table 4.5: Descriptive Statistics for Abnormal Returns One-Sample Statistics N Mean Std. Deviation Std. Error Mean AR 1997 30 .0000000 .51479428 .09398815 AR 2002 30 .0000000 1.51935929 .27739579 AR 2007 30 .0000000 1.80390395 .32934629 AR 2013 30 .0000000 1.16988689 .21359115 The descriptive statistics for the variables have been provided as the number of observations (N), the mean and the standard deviation for the 1997, 2002, 2007 and 2013 general elections abnormal returns (AR). The standard error is the estimated deviation of the mean of the sample used for the statistical test. For the 1997 general election abnormal returns (AR), the standard error of the sample mean is merely 0.093 which is relatively small. Therefore, there is a high likelihood that the sample mean is close to the population mean. The standard error of the sample mean for the 2002 general election abnormal returns (AR) is 0.277 which is relatively small meaning that it too adequately represents the population mean. Similarly, the standard errors for 2007 and 2013 general elections abnormal returns are 0.329 and 0.214 respectively which are relatively small meaning that they too adequately represents the population mean. Table 4.6: T – test on Abnormal Returns One-Sample Test Test Value = 0 95% Confidence Interval of the Difference t df Sig. (2-tailed) Mean Difference Lower Upper AR 1997 .000 29 1.000 .00000000 -.1922273 .1922273 AR 2002 .000 29 1.000 .00000000 -.5673381 .5673381 AR 2007 .000 29 1.000 .00000000 -.6735888 .6735888 AR 2013 .000 29 1.000 .00000000 -.4368429 .4368429 38 This output gives the t-test value, the degrees of freedom and the two-tailed significance. Since the p values for 1997, 2002, 2007 and 2013 abnormal returns are all 1.000 which are more than 0.05, the null hypothesis cannot be rejected. The t-statistical test at 5% level of significance indicates that event - day abnormal returns (AR) were insignificant for the general elections. 4.7.2 T – test for Cumulative Abnormal Returns Table 4.7: Descriptive Statistics for Cumulative Abnormal Returns One-Sample Statistics N Mean Std. Deviation Std. Error Mean CAR 1997 30 -.5807411 1.43411591 .26183254 CAR 2002 30 1.1008060 4.53934033 .82876636 CAR 2007 30 3.0701212 2.11975709 .38701293 CAR 2013 30 -.5648116 2.49004039 .45461710 The descriptive statistics for the variables have been provided as the number of observations (N), the mean and the standard deviation for the 1997, 2002, 2007 and 2013 general elections cumulative abnormal returns (CAR). For the 1997 general election cumulative abnormal returns (CAR), the standard error of the sample mean is merely 0.261, for the 2007 general election cumulative abnormal returns (CAR), the standard error of the sample mean is merely 0.387 which is relatively small, for the 2013 general election cumulative abnormal returns (CAR), the standard error of the sample mean is merely 0.455. For these years (1997, 2007 and 2013), there is a high likelihood that the sample mean is close to the population mean. The standard error of the sample mean for the 2002 general election cumulative abnormal returns (CAR) is 0.828 which indicates that it is not adequately representative of the population mean. 39 Table 4.8: T – test for Cumulative Abnormal Returns One-Sample Test Test Value = 0 95% Confidence Interval of the Difference t df Sig. (2-tailed) Mean Difference Lower Upper CAR 1997 -2.218 29 .035 -.58074115 -1.1162488 -.0452335 CAR 2002 1.328 29 .194 1.10080601 -.5942115 2.7958235 CAR 2007 7.933 29 .000 3.07012117 2.2785909 3.8616515 CAR 2013 -1.242 29 .224 -.56481160 -1.4946080 .3649848 4.8 Summary and Interpretation of the Findings 4.8.1 Summary The objective of this study was to investigate whether there is a relationship between the general elections and the market performance for firms listed in the Nairobi Securities Exchange. The t-statistic test shows that for all the four general elections (100%) namely the 1997, 2002, 2007 and 2013, abnormal returns were statistically insignificant. The Cumulative abnormal returns for the 1997 and 2007 general elections were found to be statistically significant while the cumulative abnormal returns for the 2002 and 2013 general elections were established to be statistically insignificant at 5% level of significance. This finding may imply that Nairobi stock exchange market viewed the 2002 and 2013 general election events as inconsequential and hence rebounded and stabilized immediately, hence the insignificance of cumulative abnormal returns (CAR) of the 2002 and 2013 general elections. 40 The findings suggest that the NSE 20 share index as well as the individual stock returns for the 1997 and 2007 general elections deviated significantly from their means while those for the 2002 and 2013 elections showed no significant deviation from their means. 4.8.2 Interpretation The t test values for the 1997, 2002, 2007 and 2013 general elections cumulative abnormal returns (CAR) are calculated as -2.218, 1.328, 7.933 and -1.242 respectively. Since the p values for 1997 and 2007 cumulative abnormal returns are 0.035 and 0.000 respectively which are less than 0.05, the null hypothesis is rejected. However, for 2002 and 2013 cumulative abnormal returns (CAR), the p values of 0.194 and 0.224 respectively are greater than 0.05 and therefore, the null hypothesis cannot be rejected. As per the t-statistical test for the cumulative abnormal returns (CAR), the 2002 and 2013 general elections were found to be insignificant while the 1997 and 2007 general elections were found to be significant at 5% level of significance. This means that the NSE 20 share index as well as the individual stock returns for the 1997 and 2007 general elections deviated significantly from their means while those for the 2002 and 2013 elections showed no significant deviation from their means. 4.8.3 Previous Studies Earlier empirical studies showed that the average cumulative abnormal returns exhibited a reducing trend in the periods preceding announcement and a slower increase after announcement pointing to market absorption of the information in the long run period after the announcement (Irungu, 2012). Menge, et al. (2014) studying the effect of elections on stock market returns at the Nairobi securities exchange adopting events study methodology covering periods between 2002 and 2013 found that actual stock returns were significantly higher before elections than after election periods. These studies are consistent with the findings of this study. 41 CHAPTER FIVE SUMMARY, CONCLUSIONS AND RECOMMENDATIONS 5.1 Summary The study finds that the NSE 20 share index has exhibited seasonality over the months from the year 1997 to 2013. The returns are exhibiting seasonality and volatility over the periods. For all the four events (general elections), in two events (50%) the normal and the abnormal returns move in uniformity while in other two events (50%), the normal and the abnormal returns have the same trend as they move in synchronicity. For three of the events (75%), the pre-election days have negative cumulative abnormal returns which turn to positive after the election. For one election, Year 2007 election, the cumulative abnormal returns are positive before the election and turns negative briefly after the election after which the returns swing to positive and declines in the remainder of the period. The t-statistic test shows that for all the four general elections (100%) namely the 1997, 2002, 2007 and 2013, abnormal returns were statistically insignificant. The Cumulative abnormal returns for the 1997 and 2007 general elections were found to be statistically significant while the cumulative abnormal returns for the 2002 and 2013 general elections were established to be statistically insignificant at 5% level of significance. This finding may imply that Nairobi stock exchange market viewed the 2002 and 2013 general election events as inconsequential and hence rebounded and stabilized immediately, hence the insignificance of cumulative abnormal returns (CAR) of the 2002 and 2013 general elections. The findings suggest that the NSE 20 share index as well as the individual stock returns for the 1997 and 2007 general elections deviated significantly from their means while those for the 2002 and 2013 elections showed no significant deviation from their means. 42 5.2 Conclusions From the findings, it is concluded that market reaction to elections is highly negative or positive depending on the election at hand and hence the information made by general election is useful for valuing the securities in the markets. For example, in the 2007 general elections, the abnoral return declined to -4.936 on the first day of trading after the election. It can thus be observed that election affects the performance of the stock market and hence shareholders and other stakeholders should not overlook electioneering events. Volatile elections negatively affects transactions processing in the market place and impedes the buying and selling of securities by instilling fear on investors as well as destroying infrastructural facilities. This is depicted from the sharp decline of the abnormal returns in the post event period after the 2007 elections noted in the study. The information made by election announcements is useful for valuing the stocks although the market does not price the information contained in some elections such as the one in 2002 and 2013. The cumulative abnormal returns exhibited a reducing trend in the periods preceding announcement and a sharp increase after announcement pointing to market absorption of the information in the long run period after the announcement. The two election periods indicate that the elections did not affect the investor confidence. The study observes that the market return is a good predictor of a stock return. It was observed that the both the abnormal returns and the return of the market moved in tándem.The market reacted with the announcements of the election results.The reaction was however positive or negative. 5.3 Recommendations Due to reforms in the election process in Kenya, the 2013 general election was credited as peaceful and as such the abnormal returns one trading day after the election rose from 0.290 to 0.408 which is a pointer to improved investor confidence that may be explained by constitutional provisions for adressing election disputes. The government should 43 therefore ensure that all the election provisions in the new constitution are implemented and adhered to. The government through the Capital Markets Authority should enact policies aimed at cushioning the market from political interferences. The policies should be aimed at encouraging more local investors into the securities Exchange. The investors should carefully analyse whether to hold or sell stocks prior or after the events of the general elections. A compromise position of the portifolio to be held during the period need to be carefully considered inorder to save the investors from losses culminating from herd behaviour in the securities market during the election periods. The government should sensitize the citizens through civic education the need to embrace democracy and hence peaceful elections. This is because undemocratic elections may lead to post elections violence hence uncertainities and adverse effect on the stock prices. Firms planning to raise capital from the securities Exchange should similarly carefully analyse the market to ensure that the timing is right. Otherwise it may coincide with a bearish market trend at the NSE. 5.4 Limitations of the Study The market performance during the elections may have been affected by other market anomalies such as the weekend effect, Monday effect, holiday effect. Three of the event periods that were analysed took place during the festive december holidays. Factors such as cash flows, growth opportunities, liquidity and dividend payouts were not considered when estimating the returns. They however affect market returns of firms. These factors were not isolated during the research and hence could have moderated the results of this study. 44 Investor behavioral biases may also have come into effect during the study period. These include issues of capital flights by investors unsure of the policies to be implemented by the new office holders. Some of the investors may use the wai and see approach during the period. These behavioral biases however were not considered during the study and may also have a moderating effect on the results of these findings. Macroeconomic performance such as inflation, shilling depreciation and global financial crisis might have also moderated the effect of these events (general election). Unfortunately, these moderating factors could not be isolated in the study owing to difficulty in doing so. 5.5 Suggestions for Further Research In addition to event study methodology, it is suggested that other approaches be adopted such as the filtered GARCH-EVT approach and the non-parametric methodology for use in the study of the effect of general election on the stock market performance. GARCHEVT approach enables one to study the event-day effect only, though it is computationally intensive. The constitution allows and stipulates that for certain clauses to be amended that a referendum must be conducted in Kenya. This study recommends that a similar study could be done on the information content of the constitutional referendum. Probably further the researcher could analyze whether the effect has similar characteristics as the national elections. Further studies could be done to analyze the performance of stock returns in non election periods to compare their performance with the periods prior to elections as it is in this study. Researchers should study the effect of terrorism events that are normally followed by travel advisories that tend to reduce inflow of foreign exchange from sectors such as tourism. 45 Discovery of oil deposits within the country has been presumed to have a positive effect on the future economic propects. This study recommends that research could be done on the information content of discovery pronouncements of oilwells in the country. It could be interesting to conduct similar studies on other neighbouring countries to see if their presidential and parliamentary elections in general cause negative abnormal returns, or if one can find a different relationship in other parts of the world. 46 REFERENCES Alesina, A. (1987). Macroeconomic policy in a two-party system as a repeated game. The Quarterly Journal of Economics, 651-678. Anderson, H. D., Malone, C. B., & Marshall, B. R. (2008). Investment returns under right-and left-wing governments in Australasia. Pacific-Basin Finance Journal, 16(3), 252-267. Białkowski, J., Gottschalk, K., & Wisniewski, T. P. (2008). Stock market volatility around national elections. Journal of Banking & Finance, 32(9), 1941-1953. Black, F. (1988). An equilibrium model of the crash. In NBER Macroeconomics Annual 1988, Volume 3 (pp. 269-276). MIT Press. Booth, J. R., & Booth, L. C. (2003). Is presidential cycle in security returns merely a reflection of business conditions?. Review of Financial Economics, 12(2), 131-159. Brown, K. C., & Harlow, W. V. (1988). Market overreaction: magnitude and intensity. Journal of Portfolio Management, 14(2), 6-13. Brown, S. J., & Warner, J. B. (1985). Using daily stock returns: The case of event studies. Journal of financial economics, 14(1), 3-31. Cahan, J., Malone, C. B., Powell, J. G., & Choti, U. W. (2005). Stock market political cycles in a small, two-party democracy. Applied Economics Letters, 12(12), 735-740. Campbell, J. Y., & Shiller, R. J. (1989). The dividend-price ratio and expectations of future dividends and discount factors. Review of financial studies, 1(3), 195228. Campello, D. (2007). Do markets vote? A systematic analysis of portfolio investors‟ response to national elections. Department of Political Science, University of California, Los Angeles. Chan, Y. C., & John Wei, K. C. (1996). Political risk and stock price volatility: the case of Hong Kong. Pacific-Basin Finance Journal, 4(2), 259-275. Commonwealth, (2006). The Report of the 2002 Kenya General Election Commonwealth Observer Group. Cooper, D. R., & Schindler, P. S. (2010). Business research methods. Fabozzi, F. (1995). Capital Market Institutions and Instruments. New Jersey: Prentice Hall, Inc. Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The journal of Finance, 25(2), 383-417. Fama, E., & French, K. (1988). Permanent and temporary components of stock prices. The Journal of Political Economy, 96(2), 246-273. Ferri, M. (2008). The response of US equity values to the 2004 presidential election. Journal of Applied Science, 18(1), 29-38. 47 Fischer, D. E., & Jordan, R. J. (1995). Security analysis and portfolio management. Englewood cliffs: Prentice Hall. Forester, S. & Schmitz, F. (1997). The transmission of U.S. election cycles to international stock returns. Journal of International Business Studies, 28(1), 1-27. Gemmill, G. (1992). Political risk and market efficiency: tests based in British stock and options markets in the 1987 election. Journal of Banking & Finance, 16(1), 211-231. Gilson, R. J. & Black, B. S. (1995). The Law and Finance of Corporate Acquisitions. Hensel, C. R., & Ziemba, W. T. (1995). United States investment returns during Democratic and Republican administrations, 1928-1993. Financial Analysts Journal, 61-69. Herbst, A. F., & Slinkman, C. W. (1984). Political-economic cycles in the US stock market. Financial Analysts Journal, 38-44. Huang, R. D. (1985). Common stock returns and presidential elections. Financial Analysts Journal, 58-61. http://www.nse.co.ke Nairobi Stock Exchange Website. Irungu, A. K. (2012). Informational content of general election results announcenment at the nairobi securities exchange. Unpublished MBA project, University of Nairobi. Jacobsen, B. (1999). Irrational trading in a financial market. Available at SSRN 150629. Jesen, G.R., Mercer, J.M. & Johnson, R.R. (1996). Business conditions, Monetary Policy, and Expected Security Returns. Journal of financial Economics 40: 213-237. Kenya Revenue Authority. Financial Report, 2009/2010. Kithinji, A. & Ngugi, W. (2009). Stock Market Performance before and after General Elections – A Case Study of the Nairobi Stock Exchange. Korteweg. A., (2009). The Net Benefits to Leverage. Journal of Finance, 75(1), 1-43. Leblang, David & Bumba Mukherjee. (2005). Government partisanship, elections, and the stock market: examining American and British stock returns, 1930-2000. American Journal of Political Science 49(4), 780-802. Lee, R. (1998). What Is An Exchange? The Automation, Management, and Regulation of Financial Markets. New York: Oxford University Press Inc. LeRoy, S.F. and Porter, R (1981). The Present Value Relation: Tests Based on Variance Bounds, Econometrica 49 (19), 555-577. Lusinde, M. M. (2012). Volatility in stock returns of NSE listed companies around general elections in Kenya. Unpublished MBA project, University of Nairobi MacKinlay, A. C. (1997). Event studies in economics and finance. Journal of economic literature, 13-39. 48 Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91. Marshall, C. & Rossman, G.B. (2006) Designing qualitative research (4th. Ed). London: Sage. Mc Callum, B. (1978). The political business cycle: An empirical test. Southern Economic Journal, 44(3), 504-515. Mehdian, S., Nas, T., & Perry, M. J. (2008). An examination of investor reaction to unexpected political and economic events in Turkey. Global Finance Journal, 18(3), 337-350. Mendelson, M. (1976). Investment Analysis and Security Markets. Menge, R.N, Mwangi, M. & Kimani, J.G. (2014) Effect of elections on stock market returns at the Nairobi securities exchange. Prime Journal of Social Science 3(6), 763-768. Mishkin, Frederic S. & Eugene,W. N. (2002). U.S. Stock Market Crashes and their Aftermath: Implications for Monetary Policy. National Bureau of Economic Research, Working Paper Series 8992, 1-10. Muga, D.N. (1974) The Nairobi Stock Exchange; it's History, Organization and Role in the Kenyan Economy. Unpublished MBA Dissertation University of Nairobi. Mugenda, O. M., & Mugenda, A. G. (2003). Research methods. Quantitative and qualitative approaches. Nairobi. Acts Press. Niederhoffer, V., Gibbs, S., & Bullock, J. (1970). Presidential elections and the stock market. Financial Analysts Journal, 111-113. Nofsinger, J.R., 2004. The stock market and political cycles. Paper presented at the Annual Meeting of the Financial Management Association, October 6-9, 2004, New Orleans, LA. Nordhaus. W. (1975). The political business cycle. The review of economic studies, 42(2), 169-190 Ofek, E., & Richardson, M. (2003). Dotcom mania: The rise and fall of internet stock prices. The Journal of Finance, 58(3), 1113-1138. Olowoniyi, A. O & Ojenike, J.O, (2012), Determinants of Stock Return of NigerianListed Firms, Journal of Finance, June 2012. Pantzalis, C., Stangeland, D. A., & Turtle, H. J. (2000). Political elections and the resolution of uncertainty: the international evidence. Journal of banking & finance, 24(10), 1575-1604. Peel, D. & Pope, P. (1983). General Election in the U.K. in the Post-1950 Period and the Behavior of the Stock Market, Investment Analysis 67, 4-10. Santa-Clara, P., & Valkanov, R. (2003). The presidential puzzle: political cycles and the stock market. The Journal of Finance, 58(5), 1841-1872. Shiller, R. J. (1981). The Use of Volatility Measures in Assessing Market Efficiency. The Journal of Finance, 36(2), 291-304. 49 Shleifer, A & Vishny, R.W., (2000), Agency problems and dividend policies around the world, Journal of Finance, 55(1), 1-33. Siokis, & Kapopoulos, (2007). Parties, Elections and Stock Market Volatility: Evidence from a Small Open Economy. Sunde, T. & Sanderson A. (2009). A review of the determinants of share prices. Journal of Social Sciences 5(3), 188-192. Vuchelen, J. (2003). Electoral systems and the effects of political events on the stock market: The Belgian case. Economics & Politics, 15(1), 85-102. Worthington, A. (2006). Political cycles and risk and return in the Australian stock market, Menzies to Howard (working paper). School of accounting and finance, university of Wollongong. 50 APPENDICES APPENDIX 1: LISTED FIRMS AT NAIROBI SECURITIES EXCHANGE AGRICULTURAL Eaagads Ltd Kapchorua Tea Co. Ltd Kakuzi Ltd Limuru Tea Co. Ltd Rea Vipingo Plantations Ltd Sasini Ltd Williamson Tea Kenya Ltd COMMERCIAL AND SERVICES Express Ltd Kenya Airways Ltd Nation Media Group Standard Group Ltd TPS Eastern Africa (Serena) Ltd Scangroup Ltd Uchumi Supermarket Ltd Hutchings Biemer Ltd Longhorn Kenya Ltd TELECOMMUNICATION AND TECHNOLOGY Safaricom Ltd 51 AUTOMOBILES AND ACCESSORIES Car and General (K) Ltd CMC Holdings Ltd Sameer Africa Ltd Marshalls (E.A.) Ltd BANKING Barclays Bank Ltd CFC Stanbic Holdings Ltd I&M Holdings Ltd Diamond Trust Bank Kenya Ltd Housing Finance Co Ltd Kenya Commercial Bank Ltd National Bank of Kenya Ltd NIC Bank Ltd Standard Chartered Bank Ltd Equity Bank Ltd The Co-operative Bank of Kenya Ltd INSURANCE Jubilee Holdings Ltd Pan Africa Insurance Holdings Ltd Kenya Re-Insurance Corporation Ltd Liberty Kenya Holdings Ltd British-American Investments Company ( Kenya) Ltd CIC Insurance Group Ltd 52 INVESTMENT Olympia Capital Holdings ltd Centum Investment Co Ltd Trans-Century Ltd MANUFACTURING AND ALLIED B.O.C Kenya Ltd British American Tobacco Kenya Ltd Carbacid Investments Ltd East African Breweries Ltd Mumias Sugar Co. Ltd Unga Group Ltd Eveready East Africa Ltd Kenya Orchards Ltd A. Baumann CO Ltd CONSTRUCTION AND ALLIED Athi River Mining Ltd Bamburi Cement Ltd Crown Berger Ltd E.A.Cables Ltd E.A.Portland Cement Ltd ENERGY AND PETROLEUM KenolKobil Ltd 53 Total Kenya Ltd KenGen Ltd Kenya Power & Lighting Co Ltd Umeme Ltd GROWTH ENTERPRISE MARKET SEGMENT Home Afrika Ltd 54