Differential effects of credit risk and capital adequacy ratio on profitability of the domestic banking sector in Ghana

Abstract

Examining credit risk and banks’ solvency effects on profitability are essential for overall health of the banking sector. However, earlier studies on these nexuses are not instructive given their failure to examine how credit risk and capital adequacy levels impact on profitability for the different types of banks. In addition to re-examining the effect of credit risk and capital adequacy ratio (CAR) on profitability of the overall banking sector, this study aims at determining their differential effects on profitability of local and foreign banks within Ghana’s banking sector. The study relies on data from 11 banks spanning 2006–2016 while employing the fixed effects estimation approach. Results from the study show a positive and significant effect of credit risk on profitability with huge effect for local relative to foreign banks. However, CAR negatively affects profitability of foreign banks with no apparent impact on local banks.

Keywords:

• Non-performing loans
• capital adequacy ratio
• return on asset
• return on equity
• credit risk
• profitability

Notes

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 New European Union member states considered are: Bulgaria, the Czech Republic, Hungary, Latvia, Lithuania, Poland and Romania.

2 Six countries namely: Chile, Colombia, El Salvador, Honduras, Mexico and Paraguay.

3 The local banks are four whilst the foreign banks are seven. The local banks included CAL Bank Limited, Home Finance Company (HFC) Bank Ghana Limited, UniBank Ghana Limited and Ghana Commercial Bank (GCB) Limited. The foreign banks consist of Banque Sahélo–Saharienne pour I’Investissement et le Commerce (BSIC Ghana Limited), Barclays Bank of Ghana Limited, Ecobank Ghana Limited, Guaranty Trust Bank (Ghana) Limited, Societe General Ghana Limited, Standard Chartered Bank (Ghana) Limited and Zenith Bank (Ghana) Limited.

4 For brevity, we do not show the plots of their volatility clustering but they are available upon request from the authors.

5 We do not use the generalised method of moments (GMM) owing to the number of cross-sections (N) relative to the time span (T). To use the GMM, N > T. However, our T = N = 11. More so, if we disaggregate the sample, we will have N = 7 for the foreign banks and N = 4 for the local banks which are individually less than T. To the extent that, N < T at the disaggregated level, the use of the GMM will produce bias results although it is capable of controlling for endogeneity.

6 We also use simple rolling standard deviation to extract the volatility for both market and foreign exchange risks. The results are qualitatively similar to those found here. We do not report those findings for brevity but are available upon request from the authors. The suggestion of one of the reviewers is acknowledged here.

7 We are very grateful to one of the anonymous reviewers for raising this.

Additional information

Notes on contributors

Ali Hussam Madugu

Ali Hussam Madugu holds a Master of Commerce (Banking and Finance option) degree from the University for Development Studies (UDS), Wa, Ghana. He also holds Bachelor of Laws, LLB (Hons) from Coventry University in UK. He is a Budget Analyst with the Tamale Metropolitan Assembly, Northern region, Ghana.

Muazu Ibrahim

Muazu Ibrahim holds PhD in Finance from University of the Witwatersrand, South Africa. He also holds MSc. in Development Economics from School of Oriental and African Studies (SOAS), University of London, UK. He teaches in the Department of Banking and Finance, School of Business and Law (SBL), UDS, Wa, Ghana.

Joseph Owusu Amoah

Joseph Owusu Amoah holds Master of Commerce (Accounting option) and BA in Integrated Business Studies (Accounting option) degrees both obtained from the University for Development Studies (UDS), Wa, Ghana. He served as a Research Assistant in the School of Business and Law (SBL), UDS. Ghana.