Kenyan Stock Market Research Articles

Macroeconomic Factors and Stock Price Volatility at Nairobi Securities Exchange

The stock price volatility has been a problem in stock markets affecting the stock returns of firms listed at Nairobi Securities Exchange (NSE). Although some scholars have focused on the issue of high share price volatility, the issue persists and still hinders the development of the market. This study main objective is to determine the effects of macroeconomic factors on volatility of stock prices of stock market in Kenya. The specific objectives are; to establish the effects of inflation on stock prices of stock market in Kenya; to establish the effects of interest rates on volatility of stock prices of stock market in Kenya; and to establish the effects of exchange rates on stock prices of stock market in Kenya. The research was anchored on four theories and two models namely the Arbitrage pricing theory, Modern portfolio theory, information cascade theory, Fisher’s theory, Efficient market hypothesis and present value model. The research applied a causal research design, which followed the positivism philosophy of research and knowledge creation. The study focused on analyzing the effects of macroeconomic factors on volatility of stock prices of the NSE 20 share index in Nairobi Securities Exchange over a period ranging from 2009 to 2021. The design allowed collection of monthly data on the study variables and time series analysis of the hypothesized relationships between independent and dependent variables and conducted both descriptive statistical analysis and inferential statistical analysis. Descriptive statistical analysis involved generation of central tendency statistics such as mean, minimum and maximum points of the data as well as standard deviations to show the spread of the data from the mean. The inferential statistical analysis utilized in this study was Error Correction Model, guided by the results of cointegration tests. From the descriptive statistical analysis, the study found that between 2009 and 2021, interest rates demonstrated an average of approximately 9.155, inflation averaged around 150.916, foreign exchange rate maintained an average of roughly 94.219, and stock price volatility averaged 0.4%. The Error Correction Model revealed that, interest rates have no significant effect on stock price volatility of NSE 20 share index (β = -0.00034, p.value = 0.805). Similarly, inflation has no significant effect on stock price volatility of NSE 20 share index (β = -0.0000394, p.value = 0.218). However, foreign exchange has negative and significant effect on stock price volatility in Kenya (β = -0.0139, p.value = 0.017). The study concluded that interest rates and inflation have no significant effect on stock price volatility in Kenyan stock market, while foreign exchange has inverse and significant effect on stock price volatility. Thus, the study recommends that market participants, including government, investors, traders, policy makers and financial institutions, should incorporate foreign exchange rate movements into their risk management strategies. Adequate measures and policies such as market circuit breakers and enhanced risk management protocols should be in place to ensure smooth market operations and prevent any disruptions due to changing foreign exchange dynamics

African stock markets’ connectedness: Quantile VAR approach

The present paper investigates African stock markets’ linkages by considering stocks in the continent’s largest economies, specifically Egypt, Kenya, Morocco, Nigeria, South Africa, and Tunisia. Using a dataset that spanned November 25, 2008, to September 18, 2023, the quantile connectedness approach of Chatziantoniou et al. (2021) is employed, and the results unfold these interesting dynamics of African market connectivity: (i) In the bearish market phase, South African stock dominated the entire network, transmitting shocks to the remaining stocks, while Moroccan and Kenyan stocks played similar role mildly. (ii) In the bullish market phase, Nigerian stock dominated the market as a major net transmitter of shock supported by South African and Kenyan stock markets. (iii), The Egyptian and Tunis stock markets are net shock receivers in both the bear and bull market phases. (iv), At the median quantile value, stocks become less riskier and the Kenyan stock market becomes the most vulnerable while Nigerian, Egyptian, and South African stock markets are influenced by other stock markets when markets are calm. (v), Though, African stocks are underperforming, interested portfolio managers will learn from the trading strategies to be adopted to maximize their returns. These findings will benefit portfolio managers, international stakeholders, and regulators.

An Analysis of the Long-Run Performance IPOs and Effects in the Kenyan Stock Market

Listing of a company in the securities exchange has been observed to be followed by underpricing in the first day and long term period of underperformance in terms of pricing in the subsequent days. Consequently, there has been a considerable curiosity from stakeholders, investors and academics to comprehend the assessments of why companies go public and the issues surrounding the short and long-run performance of newly issued equities. Underpricing is necessary to induce uninformed investors to participate in IPO offering when faced with adverse selection from informed investors. This often leads to first day price not reflecting a fair value of the IPO. The objective of the study was to determine the long-run performance IPOs and effects in the Kenyan stock market for the period 2007-2014. A descriptive survey research design was employed in the study. The population of the study encompassed all the 64 listed companies at the NSE as at 2016. The study employed a non-probability purposive sampling technique. Data collected for this study was secondary data obtained from NSE website, NSE price lists and the Central Bank of Kenya website for the period 2007 to 2014. The data obtained was analyzed using Statistical Package for Social Science (SPSS). Mean Average Buy and Hold Returns (MABHR), Abnormal Returns (AR) and Cumulative Abnormal Returns (CAR) were used to calculate the performance of the stocks. T-statistic for CAR was computed to the test for its significance. T-test was conducted at 95% confidence level to find if MABHR and CAR were statistically significant after IPOs announcement.

Dynamic Linkages of Stock Market Sectors: Evidence from the Kenyan Stock Market

This paper undertakes an empirical investigation into the dynamic linkages of the Nairobi Securities Exchange’s sectors. Using weekly indices for the 9th June 2008 to 14th February 2019 period, it examines the dynamic linkages of the Investment, Manufacturing and Allied, and Banking sectors. The study finds out the presence of one cointegrating relationship in the long run. A Vector Error Correction Model is fitted to estimate the temporal relationships of the three indexes. Results of Granger Causality testing, which are based on the VECM output, indicate the presence of bidirectional granger causality between the Manufacturing and Allied & Banking sectors as well as the Banking & Investment sectors. However, there is no granger causality between the Investment & Manufacturing and Allied sectors. Impulse response analysis show that shocks to the Manufacturing and Allied Sector are least significant in terms of their ability to invoke responses from other sectors, while shocks to the Banking sector are most influential as far are the tendency of elevating the volatility of other sectors’ indexes is concerned. Forecast Error Variance Decomposition (FEVD) analysis further indicate that shocks to the banking sector are most influential in terms of their explanatory power of other sectors’ forecast variances. The study therefore concludes that the banking sector has the highest tendency to influence other sectors’ volatility, while the Manufacturing and Allied Sector is least significant in terms of its ability to influence other sectors’ volatility. Therefore, it is recommended that that stocks that are listed under the Manufacturing and Allied Sector should be considered for diversification purposes due to the low scale of linkages that this sector has with other sectors. But, better still; investors should seek inter-market diversification opportunities in order to tap fully into the benefits of portfolio diversification.

Foreign equity flows and stock market liquidity in Kenya

In this paper, we explore the dynamic relationship between aggregate foreign equity inflows and aggregate liquidity of the Kenyan stock market using transactional foreign trading data and several l...

Modelling the Stock Price Volatility Using Asymmetry Garch and Ann-Asymmetry Garch Models

The stock price in developing countries, especially in Kenya, has become one of the market that supports the economy growth of a country. Due to the political instabilities in the Kenyan contest, stock price markets have been affected. As a consequence of the instabilities in the financial markets, this paper model the volatility associated with the stock price for a one day ahead volatility forecast which will help in risk control in the market. This is accomplished by using the asymmetry GARCH and ANN-asymmetry GARCH models. The residuals obtained from artificial neural network are used when fitting ANN- asymmetry GARCH models. It was found that returns on the selected companies in NSE are categorized by volatility clustering, leptokurtosis and asymmetry. In the modelling, we further examine the performance of the leading alternatives with the daily log returns residuals of the leading companies in Kenyan stock market (PAFR, PORT and EGAD) from the period January 2006 to November 2017 for trading days excluding weekends and holidays. The root mean squared error indicated that among the available models i.e. ANN-EGARCH model, GJR-GARCH and EGARCH model, ANN-GJR-GARCH model performed better in modelling and forecasting the stock price volatility in Kenyan contest. The paper demonstrates that combined machine learning and statistical models can effectively model stock price volatility and make reliable forecasts.

A Comparative Analysis of Macroeconomic Variables and Stock Market Performances in Africa (2000-2015)

The study aimed at evaluating the impact of macroeconomic variables on stock market performance in Africa from the period of 2000 to 2015. Four major African countries investigated were: Ghana, Kenya, South Africa and Nigeria. The specific objectives were to establish the extent to which GDP, inflation rate and real exchange rate affect the stock market performance represented by share price index. Time series data were employed and analyzed using multiple regression and t-test for hypotheses testing. With the use of SPSS software the result revealed negative impact of GDP, inflation and real exchange rate on SPI in Nigeria. Insignificant relationship of all the variables was also observed. The result for South Africa’s stock market showed that GDP and inflation had a negative impact on stock market and real exchange rate has no impact on the stock market. The impact of GDP on Ghana’s stock market was negative while the others had no impact. Real exchange rate had negative impact on Kenyan stock market, but GDP and inflation had no impact. The researchers therefore conclude that macroeconomic variables have to be checked by the government of the African countries to avoid this scenario of negative effects since they are major determinant of the success of the stock markets in every economy.

The Global Financial Crisis, Inflation Rate And Stock Market Returns In Kenya

The moderating effect of events such as the 2008 Global Financial Crisis (GFC) on the relation between stock market returns and macroeconomic variables has attracted very little attention. This study investigates the extent to which the 2008 GFC moderated the relationship between inflation rate and stock market returns. The study uses month-onmonth inflation rate and year-on-year inflation rate from 1st January 1993 to 31st December 2015 and divides the sample data into pre-crisis period (from 1st January 1993 to 31st December 2007); crisis period (from 1st January 2008 to 30th June 2009); and post-crisis period (from 1st July 2009 to 31st December 2015). It uses a product-term regression model instead of the most widely applied additive regression model. Results indicate that a unit increase in the both measures of inflation rate had significant depressing effects on stock market returns after the crisis compared to before the crisis. Likewise, the results reveal that average stock market returns were significantly higher after the crisis compared to before the crisis at low rather than medium or high values of the two measures of inflation rate. These results suggest that the Kenyan stock market is highly sensitive to variations in inflation rate, especially as it emerges from a financial or political turmoil. This study is empirically innovative in the sense that it is the first to examine the moderating effect of the 2008 GFC on the relation between inflation rate and stock market returns in Kenya using a product-term model.

EFFECTS OF EDUCATION ON THE PERFORMANCE OF SMALL ENTERPRISES IN KENYA: A CASE STUDY OF GIKOMBA MARKET, NAIROBI.

Purpose: The key objective of this research was to establish the relationship, if any, between education/training and the performance of small enterprises in Gikomba market.Methodology:This study adopted descriptive and regression analysis design and the target population for this study are the owners/managers of small business enterprises in Gikomba market, Nairobi. A sample of 68 respondents will be selected. Probability sampling was used whereby stratified random sampling will be grouped into two or more relevant strata. This study used both primary and secondary data collected using questionnaires and secondary data collection data guide. Primary data was collected for all variables for a period of 3 years (2010 to 2012). Data analysis was used using both descriptive and inferential statistical methods. Descriptive statistics will include; frequencies, mean and standard deviation. Data analysis output was presented using graphs and tables. Inferential statistics will include regression and ANOVA tests.Results:The results indicate that Education greatly influences the financial and non-financial factors in the SMEs sector. However the level of education is not the key factor to SME’s existence and success. If the business management training education is well implemented, all the small enterprise traders at Gikomba open air market should be in position to breakeven and also to manage their business performance as expected.Policy recommendation: The study recommends that policy makers should adopt the findings of this study. According to the results, exchange rates, public debt and interest rates were the significant determinant of market capitalisation of listed companies in Kenya during the study period. Macroeconomic variables should be factored when formulating policies on market capitalisation. This study recommended that, since the Kenyan stock market is not really exposed to the negative effects of currency volatility, government can use exchange rate as a policy tool to attract foreign portfolio investment.

Portfolio Optimization under Threshold Accepting: Further Evidence from a Frontier Market

Several improvements and alternatives to Mean Variance Optimization (MVO) have been suggested and used since its inception in 1952. The improvements have mostly included addition of constraints to the traditional MVO model, using alternative risk measures and using non risk-reward models. This paper seeks to compare MVO against the Threshold Accepting model, which is a general optimization model, in portfolio selection. Using data on 29 stocks in the Kenyan stock market we compare the relative performance of the two models using performance measures such as the Sharpe Ratio, Sortino Ratio and Information Ratio. We find that the Threshold Accepting (TA) model outperforms the Mean-Variance Optimization model though MVO yields similar results when we use monthly or weekly data but the latter is observed as a more consistent model. The TA model has portfolios with generally more superior risk-adjusted returns for the full period and during periods of high volatility in the stock market performance market. This observation implies that relatively more attention should be given to the TA model rather than relying entirely on the classical MVO approach.

Modeling Stock Market Volatility Using GARCH Models: A Case Study of Nairobi Securities Exchange (NSE)

The aim of this paper is to use the General Autoregressive Conditional Heteroscedastic (GARCH) type models for the estimation of volatility of the daily returns of the Kenyan stock market: that is Nairobi Securities Exchange (NSE). The conditional variance is estimated using the data from March 2013 to February 2016. We use both symmetric and asymmetric models to capture the most common features of the stock markets like leverage effect and volatility clustering. The results show that the volatility process is highly persistent, thus, giving evidence of the existence of risk premium for the NSE index return series. This in turn supports the positive correlation hypothesis: that is between volatility and expected stock returns. Another fact revealed by the results is that the asymmetric GARCH models provide better fit for NSE than the symmetric models. This proves the presence of leverage effect in the NSE return series.

Application of Conditional Autoregressive Value at Risk Model to Kenyan Stocks: A Comparative Study

Value at Risk (VaR) became the industry accepted measure for risk by financial institutions and their regulators after the Basel I Accords agreement of 1996. As a result, many methodologies of estimating VaR models used to carry out risk management in finance have been developed. Engle and Manganelli (2004) developed the Conditional Autoregressive Value at Risk (CAViaR) which is a quantile that focuses on estimating and measuring the lower tail risk. The CAViaR quantile measures the quantile directly in an autoregressive framework and applies the quantile regression method to estimate the CAViaR parameters. This research applied the asymmetric CAViaR, symmetric CAViaR and Indirect GARCH (1, 1) specifications to KQ, EABL and KCB stock returns and performed a set of in sample and out of sample tests to determine the relative efficacy of the three different CAViaR specifications. It was found that the asymmetric CAViaR slope specification works well for the Kenyan stock market and is best suited to estimating VaR. Further, more research needs to be carried out to develop e a satisfactory VaR estimation model.

Stock Returns And Volatility İn An Emerging Equity Market. Evidence From Kenya

This study investigates volatility pattern of Kenyan stock market based on time series data which consists of daily closing prices of NSE Index for the period 2ndJanuary 2001 to 31st December 2014. The analysis has been done using both symmetric and asymmetric Generalized Autoregressive Conditional Heteroscedastic (GARCH) models. The study provides evidence for the existence of a positive and significant risk premium. Moreover, volatility shocks on daily returns at the stock market are transitory. We do not find any significant leverage effect. Introduction of the new regulations on foreign investors with a 25% minimum reserve of the issued share capital going to local investors (in 2002), introduction of live trading, cross listing in Uganda and Tanzania stock exchange (in 2006) and change in equity settlement cycle from T+4 to T+3 (in 2011) significantly reduce volatility clustering. The onset of US tapering increase the daily mean returns significantly while reducing conditional volatility.

The Kenyan stock market: inefficiency, long memory, persistence and anomalies in the NSE-20

This paper examines the statistical properties of the NSE-20 index in the Kenyan stock market over the period 2001 to 2009. The analysis applies both unit root tests and long-range dependence techniques based on the concept of fractional integration. The results indicate that the order of integration of stock prices is significantly above 1, which implies the presence of long memory. This is also detected in the absolute and squared returns. The lowest degrees of integration (very close to zero) are found for Mondays and Fridays, and therefore, a day-of-the-week-effect appears to be present.

The Dynamics Of Stock Market Development In Kenya

This paper highlights the origin of the stock market in Kenya, and traces the reforms that have been undertaken to develop the stock market. It also highlights the growth of the Kenyan stock market, as well as the challenges currently facing the market. The country has one stock market, known as the Nairobi Securities Exchange (formerly the Nairobi Stock Exchange). It is one of Africas largest stock markets. Since the early 1980s, a number of stock market reforms have been implemented in Kenya. These include the formation of a regulatory body (Capital Markets Authority CMA) in 1989, the replacement of the "Call-Over" trading system by the floor-based "Open-Outcry System" in 1991, the reduction of listing costs, the relaxation of the exchange control for locally controlled companies, and the repeal of the Exchange Control Act. Following these reforms, Kenyas stock market has developed significantly in terms of market capitalisation, the total value of stocks traded, and the turnover ratio. Although the stock market in Kenya has developed over the years, like many other developing countries' markets, it still faces a number of wide-ranging challenges.

Do dividends matter? Some evidence from an emerging market

This study investigated the information content theory of dividends in the Kenyan Stock market. Data was obtained from the Nairobi Stock Exchange (NSE) hand book for the period 2001–2007. Using regression analysis, the study attempted to determine the factors that influence dividend payments in the subject companies. The results of this study indicate that dividend payments are mainly influenced by the current year’s income followed by the company’s retained earnings. The study also found a significant relationship between the share prices of the companies and the dividends paid. We conclude that whereas profitability drive share prices, dividends also influence the share prices of the companies listed on the NSE. Therefore managers may use dividends to convey information. However the study found no significant relationship between dividend payment and liquidity.

Day-of-the-Week and Month-of-the-Year Effect on the Kenyan Stock Market Returns

Capital markets are normally assumed to be efficient in relation to the instantaneous incorporation of all known and new arriving information into prices of securities. Studies assessing the efficiency of capital markets have reported mixed results, some of which are against the efficient markets theory. The purpose of this study was to determine if daily and monthly seasonal anomalies do exist in the Kenyan stock market. Data on prices and adjusted returns derived from the NSE 20 index were analysed using regression analysis to identify the behaviour of stock investors in Kenya during 1980–2006. Results indicate that Monday produces the lowest negative returns, while Friday and January produce the largest positive returns. These results are useful in providing evidence of deviation from the efficient markets theory and in drawing conclusions about anomalies in an emerging stock market. Finding highest return volatility on Friday and lowest on Monday might be due to several economic news announcements released on Thursdays and Fridays, and is consistent with informed trader argument. The returns are therefore influenced by foreign portfolio investor behaviour and delays in receiving news released from foreign financial markets. Day-of-the-week effect and January effect patterns in return and volatility might enable investors to take advantage of relatively regular shifts in the market by designing trading strategies, which accounts for such predictable patterns.