Abstract
The study investigates the effect of corporate governance on financial distress in the Nigerian banking industry and examines the discriminatory power of corporate governance mechanism of the board, audit committee, executive management and auditor in one model for financial distress prediction. Secondary data obtained from annual financial statements of twenty banks between 2005 and 2015 were used for the study. The data were analyzed using descriptive statistics and generalized quantile regression model. The empirical evidence from the study suggests that financially distressed banks are characterized by large board size with members who may not be well versed in banking complexities, chairmen and CEOs with significant shareholding both individually and collectively. Furthermore, the evidence also shows that distressed banks suffer major decline in customer deposits despite increase in size. The study concludes that financial distress can be caused by poor corporate governance mechanism.
Highlights
Banks play a very important role in the society, occupying critical position in the process of promoting economic growth (Wanke, Barros and Faria, 2015)
This study examined the effect of corporate governance on financial distress in the Nigerian banking industry
We analyzed a sample of 20 banks over the period between 2005 and 2015 and measured financial distress by data envelopment analysis (DEA) technical efficiency following previous studies and corporate governance variables along board characteristics, ownership structure, shareholding, external audit opinion and control variables
Summary
Banks play a very important role in the society, occupying critical position in the process of promoting economic growth (Wanke, Barros and Faria, 2015). As a result of this role, a properly functioning banking sector is crucial for the growth of an economy and the stability of the financial system (Hoggarth, Reis and Saporta, 2002) National governments through their regulatory agencies have shown concern towards the proper functioning of the banking industry and have regulated the industry. Various models have been used in financial distress prediction starting with diverse statistical methods such as Altman’s (1968) multiple discriminant analysis, Ohlson’s (1980) logistic regression; Intelligent models such as neural network model, support vector machine, genetic algorithm, genetic programming and others. All of these methods focused on the explanatory powers of financial, accounting and market variables (Manzaneque, Priego and Merino, 2016)
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