A new two-stage hybrid approach of credit risk in banking industry

The rationale under the analyses is to propose a new approach by three kinds of two-stage hybrid models of logistic regression-ANN, to explore if the two-stage hybrid model outperformed the traditional ones, and to construct a financial distress warning system for banking industry in Taiwan. The differences from the literatures are that this study adopts the “optimal cutoff point” approach proposed by Hosmer and Lemeshow [Hosmer, D. W., & Lemeshow, S. L. (2000). Applied logistic regression (2nd ed.). New York: A Wiley-Interscience], to determine the cutoff point for financial distress. Additionally, cross-validation [Efron, B., & Tibshirani, R. J. (1993). An introduction to the bootstrap. New York: Chapman and Hall; Stone, M. (1974). Cross-validation choice and assessment of statistical predictions. Journal of Royal Statistical Society. Series B, 36, 111–147] is used to evaluate the prediction power of the constructed models. sults find the factors of observable loans to total loans, allowance for doubtful accounts recovery rate, and interest-sensitive assets to liabilities ratio are significantly related to the financial distress of banks in Taiwan. In the prediction of financially distressed, two-stage hybrid model giving the best performance of 80.0% using cross-validation approach and demonstrates stronger prediction power than conventional logistic regression, logarithm logistic regression, and ANN approaches. It demonstrates that the two-stage hybrid model outperforms the conventional method, providing an alternative in handling credit risk modeling which have assessment implications for analysts, practitioners, and regulators.