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

Deron Liang, Chia-Chi Lu, Chih-Fong Tsai and Guan-An Shih

European Journal of Operational Research, 2016, vol. 252, issue 2, 561-572

Abstract: Effective bankruptcy prediction is critical for financial institutions to make appropriate lending decisions. In general, the input variables (or features), such as financial ratios, and prediction techniques, such as statistical and machine learning techniques, are the two most important factors affecting the prediction performance. While many related works have proposed novel prediction techniques, very few have analyzed the discriminatory power of the features related to bankruptcy prediction. In the literature, in addition to financial ratios (FRs), corporate governance indicators (CGIs) have been found to be another important type of input variable. However, the prediction performance obtained by combining CGIs and FRs has not been fully examined. Only some selected CGIs and FRs have been used in related studies and the chosen features may differ from study to study. Therefore, the aim of this paper is to assess the prediction performance obtained by combining seven different categories of FRs and five different categories of CGIs. The experimental results, based on a real-world dataset from Taiwan, show that the FR categories of solvency and profitability and the CGI categories of board structure and ownership structure are the most important features in bankruptcy prediction. Specifically, the best prediction model performance is obtained with a combination in terms of prediction accuracy, Type I/II errors, ROC curve, and misclassification cost. However, these findings may not be applicable in some markets where the definition of distressed companies is unclear and the characteristics of corporate governance indicators are not obvious, such as in the Chinese market.

Keywords: Bankruptcy prediction; Financial ratios; Corporate governance indicators; Feature selection; Data mining (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (81)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:252:y:2016:i:2:p:561-572

DOI: 10.1016/j.ejor.2016.01.012

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