Determinants of bank shareholder value: evidence from GCC countries

Purpose - The purpose of this paper is to examine the effect of bank specific, financial structure and macroeconomic factors on the shareholder value of banks in GCC economies during 2000–2017. Design/methodology/approach - To estimate the model and analyze the data collected from the BankScope and World Bank World Development Indicator database, the author uses static panel estimation techniques as well as two-step difference and system dynamic generalized method of moments estimator. Findings - The results show that banks that are highly dependent on non-traditional activities have higher shareholder value. Higher opportunity cost, capitalization and demand deposits result in a better bank shareholder value. Furthermore, banks with higher loan exposure and growth have better shareholder value. Non-performing loans and market risk have insignificant effects on bank shareholder value. However, GCC banks suffer from diseconomies of scale and scope. The author also finds that banks located in countries with high inflation rates, high rates of interest or in financially developed economies offer better shareholder value. High credit to the private sector reduces the bank shareholder value. The paper also provides evidence that the impact of financial turmoil on the shareholder value of the GCC banking sector is negative and significant and has severely weakened the GCC banking system. Practical implications - The results of this study necessitate formulation of various policy measures that can counter the effects of shareholder value of banks. Originality/value - The present study is among the first to address the influence of financial turmoil on bank shareholder value. It also studies new variables, such as demand deposits, non-performing loans, loan growth, non-interest revenue and off-balance sheet activities, which have not been examined in relation to bank shareholder value. It also applies both static techniques and dynamic panel estimation techniques to analyze the data. The analysis is carried out at the aggregate level as well as at the national level and also provides several robustness analyses using various model specifications.