Redefinition of the KMV model's optimal default point based on genetic algorithms - Evidence from Taiwan

Authors:
Wo-Chiang Lee
Affiliations:
Department of Banking and Finance, Tamkang University, 151, Yin-Chuan Road, Tamsui, New Taipei City 25137, Taiwan, ROC
Venue:
Expert Systems with Applications: An International Journal
Year:
2011

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Abstract

In this paper, we propose a new method based on genetic algorithms to solve the optimal default point of the KMV model. In our empirical study, we compare the GA-KMV model with the QR-KMV and KMV models. The results indicate that the percentage of correctness of the GA-KMV model is higher than those for the other two models. This is to say, the GA-KMV model has a better goodness of fit. We also obtain the optimal default point for a Taiwan listed company. This can help us to predict the default point and improve the bank's risk management performance.