Investigating the dynamic impact of firm-specific and macroeconomic drivers on profitability of general insurance companies in Bangladesh

Abstract

This paper aims to investigate the relationship between profitability measured with ROE along with EPS and several industry-specific factors such as underwriting risk, reinsurance dependence, solvency margin, leverage, liquidity risk, premium growth, size of the company, tangibility of assets as well as macroeconomic factors such as GDP growth, inflation, and stock market development of the general insurance companies of Bangladesh based on the sample of 7 insurance companies for the period of 2010-2019. For the analyses, several econometric models have been adopted to estimate the coefficients such as Pooled OLS, Cross-sectional GLS, Fixed-effect, Random-effect, and One-step GMM approaches. Moreover, diagnostic tests have also been conducted to examine the validity of the models such as model specification tests, heteroskedasticity tests, multicollinearity tests, autocorrelation tests, and unit-root tests. According to the computed coefficients, none of the macroeconomic variables utilized in the model affects profitability, except inflation, which positively affects ROE. Underwriting risk and size are found to negatively affect ROE but positively affect EPS. Of the two measures of profitability, only the EPS is affected by premium growth. Except for Fixed-effect, reinsurance dependence has a large positive influence on EPS and ROE. In contrast, liquidity and leverage have a positive impact on ROE. The tangibility of assets negatively affects both EPS and ROE where EPS is significant and ROE is insignificant only under fixed effect. Aside from premium growth, underwriting risk and solvency margin have a positive impact on EPS. Finally, using the GMM technique, we find that no lag variables are significant, indicating no profitability persistence. However, the model fits the data well for predicting EPS and ROE as profitability measures explaining 89.38 percent of EPS variation and 80.38 percent of ROE variation using Pooled OLS.