Effect of Market Risk on Market Returns of Equity Securities Market in Kenya

The study aimed to establish the effect of market risk on market returns of securities traded in Kenyan securities exchange market. The study used panel data analysis to evaluate specific objectives. These included to evaluate the effect of market risk on the market returns of equity securities in Kenya. The study used secondary data from all the firms listed in NSE during the period 2004 to 2016. The target population of the study consisted of the 64 companies listed in Nairobi securities exchange market that is, both financial and non-financial companies. The study was a census study of all the 64 companies listed in the Nairobi security exchange market for 13 years starting the year 2004 to the year 2016. The study started with descriptive and then diagnostic tests. The measures of central tendency used to test normality were mean, median, maximum and minimum value, standard deviation, skewness and kurtosis. The results from these tests showed that the variables were fairly normally distributed. The study further sought to investigate the stationality properties of market returns, trading volume, trading activity, market risk and trading volume. The study used five panel data unit root tests. Particularly the test were, Levin, Lin and Chu t, Breitung t-stat, Im, Pesaran and Shin W-stat developed, Fisher-type tests using augmented dickey fuller ADF and (Phillip and Peron) PP tests. The all these tests revealed that the variables were stationary on average. The results from granger causality showed that there was a unidirectional granger causality running from market returns to market risk. The cointegration results showed that there was long-run equilibrium. The regression technique used was the DOLS (dynamic ordinary least square method). The regression results revealed that market risk had a negative and statistically significant effect on market returns. It is therefore in this light that the future research should consider other variables which would increase the predictive power of the model. The other relevant variables would be variables such as the size of the firm, market value of the firm and the macroeconomic variables such as exchange rate, inflation, money supply among others.