Effect of Credit Risk on Shareholders’ Wealth of Listed Commercial Banks in Kenya

The financial manager’s mandate is to maximize shareholders’ wealth by investing in projects with positive cash flows. Credit risk is the potential loss incurred by a company when counterparties default in their financial obligations. This leads to cash out flow, which in turn affects shareholders’ wealth. To achieve higher returns, financial managers must take more risk. This paper focuses on determining the effect of credit risk on shareholders’ wealth of listed commercial banks in Kenya. Secondary data was collected from the annual financial statements of 11 listed commercial banks and the corporation credit ratings publications for the period 2014 to 2018. Through quantitative research design, panel data was analyzed using multiple linear regression model. The study found that capital adequacy had a significant positive effect on shareholders wealth. Non-performing loans, credit liquidity, loan loss provision, and credit ratings had an insignificant effect on shareholders wealth. Based on F statistic results, credit risk had a significant negative effect on shareholders wealth. The study recommends that financial managers should retain more capital based on the risk weighted assets which acts as a buffer if the bank incurs losses arising from credit risk. Commercial banks in Kenya should also fully adopt the Basel III Accord requirements so as to maximize the wealth of the shareholders

Trading Volume and Fama-French Three Factor Model on Excess Return. Empirical Evidence from Nairobi Security Exchange

The main objective of this paper is to examine the effect of Trading Volume on excess return using the Fama-French three factor model of listed companies in Kenya. The research study employed a Quantitative research design to analyses the effect of Trading Volume on excess returns in Nairobi Security Exchange (NSE) during the period 2006 to 2015. Secondary data was used for this study. The study utilized descriptive statistics, correlation, unit root test, Heteroscedasticity, and Autocorrelation test as diagnostic tests. The regression results revealed that Market premium and Value premium (HML) and Trading Volume have a high explanatory power while the size premium (SMB) has a low explanatory power