Banking industry stability and investment dynamics

Abstract

Purpose This paper aims to evaluate how strands of differing investments influence stability in the banking industry using data from 37 countries in Sub-Sahara Africa from 2000 to 2018. Design/methodology/approach Empirical analyses in the study were carried out using a two-step system Generalized Method of Moments estimation methodology. Findings Empirical results suggest that generally, growth in investments by governments, foreign investments and private domestic investments have a significant positive impact in stabilizing the banking industry. The empirical estimates further suggest that macroeconomic conditions such as macroeconomic uncertainty adversely affects the liquid reserve position of banks even during periods of appreciable growth in investments. Originality/value The authors present a different approach to the banking industry discourse. Instead of surmise the relationship with the direction of impact often emanating from the banking industry to other variables of interest or conditions, this study rather examines how investment dynamics among economies influence the stability of the banking industry overtime. In contrast to related studies, this study examines how strands of investment variables influence the stability of the banking industry. Specifically, this study is modeled to examine the extent to which variability in investment growth (using different investment variables) affect stability in the banking industry.

Regulatory policy uncertainty, banking industry innovations and financial development among emerging markets

“…The difference GMM estimator is biased because it uses the lagged levels of the independent variables as its instruments (Ofoeda et al , 2022). Abaidoo and Agyapong (2022a) further argue for the superiority of the GMM estimator for panel data analysis by positing that it presents results that are consistent and asymptomatically normally distributed; controls for panel data heterogeneity, controls for variable persistence and allows for the inclusion of the lagged forms of the dependent variable without loss of efficiency. Again, preference is given to the two-step variant of the system GMM estimator as compared with the one-step estimator for the analysis because, as argued by Hwang and Sun (2018), the two-step estimator provides smaller asymptotic variance; this makes it more efficient than the one-step estimator.…”