Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study

Failure of banks not only affects the banks themselves but also reaches the global economy.

“Failure of banks not only affects the banks themselves but also reaches the global economy (Liang et al. 2016).”

Little effort has been made to conduct empirical studies that test the contribution of CG indicators in failure prediction along with financial ratios.

“Liang et al. (2016) declare that, even though the importance of CG is well recognised in the literature, little effort has been made to conduct empirical studies that test the contribution of CG indicators in failure prediction along with the financial ratios.”

Failure prediction enables banks to make appropriate lending decisions.

“For example, bank failure affects the whole economic stability (Boyacioglu et al. 2009), failure prediction enables banks to make appropriate lending decisions (Liang et al. 2016) and bank failure could have been prevented if appropriate failure prediction tools had been used (Kao and Liu 2004).”

CG enhances the accuracy of prediction and improves the performance of all models utilised in Liang et al.'s study.

“Their results suggest that CG enhances the accuracy of prediction and improves the performance of all models utilised in their study.”

Corporate governance has not been examined before as a non-financial predictor of the failure of US banks.

“However, these studies were conducted on non-financial firms, and to the best of our knowledge, CG has not been examined before as a non-financial predictor of the failure of US banks.”

The most important CG indicators to predict failure are the ones related to the board and ownership structure.

“They find that the most important CG indicators to predict failure are the ones related to the board and ownership structure.”

The stratified random sampling technique accounts for the class imbalance problem caused by the difference between the number of failed and non-failed cases.

“In addition, this sampling technique accounts for the class imbalance problem caused by the difference between the number of failed and non-failed cases, which could lead to a degradation in the performance of the prediction (Liang et al. 2016).”