(The) integrated methodology of aritificial intelligence and statistical methods for bankruptcy prediction

Abstract

The phenomenon of corporate failure is not frequent nor of minor economic consequence. Under the influence of International Monetary Fund (IMF), the number of bankrupt companies extremely increases this year in Korea. It is obvious that such a crucial issue in corporate finance warrants careful investigation (Cadden, 1995). Although many studies demonstrate that one technique outperforms the others for a given data set, there is often no way to tell a priori which of these techniques will be most effective to solve a specific classification problem. Alternatively, it has been suggested that a better approach to classification problem might be to integrate several different forecasting techniques. (Jo & Han, 1996; Markham & Ragsdale, 1995) This study proposes the linearly combining methodology of different classification techniques. The methodology is developed to find the optimal combining weight and compute the weighted average of different techniques`` outputs. The proposed methodology is represented as the form of mixed integer programming. The objective function of proposed combining methodology is to minimize total misclassification cost, which is the weighted sum of two types of misclassification. To simplify the problem solving process, cutoff value is fixed and threshold function is removed. The form of mixed integer programming is solved with the genetic algorithms and branch and bound methods. The results showed that proposed methodology classify more accurately than any of techniques individually do. It is confirmed that proposed methodology predicts significantly better than individual techniques and the other combining methods.