Risk and return analysis of listed financial companies in Ghana (a study of Cal Bank, Ecobank GH Ltd, Ghana Commercial Bank ( GCB) and Standard Chartered Bank (SCB))

Abstract

ABSTRACTGlobally, Investors as well as business men and women are very optimistic about the future and therefore defer current use of resources for use at a later period with a higher expected rate of return. This is not so different in the case of Ghana. The main objective of the study is to analyse the risk and return of listed financial companies in Ghana. A sample of four financial companies listed on the Ghana Stock Exchange (Cal Bank Limited, Ecobank Ghana Limited, GCB Bank Limited and Standard Chartered Bank Ghana Limited) were selected and financial ratio (return on equity, return on asset, current ratio, quick ratio and financial leverage) computed using excel and analyzed using STATA. The study fitted a Pooled Ordinary Least Square (OLS) and Least Square Dummy Variable (LSDV) regression model with fixed effect model since fixed effect was tested to be statistically significant in the model. Empirical analysis of the data revealed that Banks with high risk levels proved to have a higher profitability as compared to the other banks under study. Standard Chartered Bank Ghana Limited and Ecobank Ghana Limited showed the highest levels of profitability, with Standard Chartered Bank Ghana Limited topping the charts in most years. However, on average, Cal Bank Ghana recorded the lowest rates of profitability.Also, a positive relationship existed between profitability (return on equity) and current ratio and financial leverage whilst an inverse relationship between return on equity and quick ratio. Overall model for both the Pooled OLS and the LSDV regression model tested to be statistically significant and a greater percentage (99.99%) of the total variability in return on equity was explained by the independent variables (return on asset, current ratio, quick ratio and financial leverage).Key words: Risk, Return, Pooled OLS regression model, LSDV regression model.