A genetic programming model for bankruptcy prediction: Empirical evidence from Iran

Abstract

Prediction of corporate bankruptcy is a phenomenon of increasing interest to investors/creditors, borrowing firms, and governments alike. Timely identification of firms’ impending failure is indeed desirable. By this time, several methods have been used for predicting bankruptcy but some of them suffer from underlying shortcomings. In recent years, Genetic Programming (GP) has reached great attention in academic and empirical fields for efficient solving high complex problems. GP is a technique for programming computers by means of natural selection. It is a variant of the genetic algorithm, which is based on the concept of adaptive survival in natural organisms. In this study, we investigated application of GP for bankruptcy prediction modeling. GP was applied to classify 144 bankrupt and non-bankrupt Iranian firms listed in Tehran stock exchange (TSE). Then a multiple discriminant analysis (MDA) was used to benchmarking GP model. Genetic model achieved 94% and 90% accuracy rates in training and holdout samples, respectively; while MDA model achieved only 77% and 73% accuracy rates in training and holdout samples, respectively. McNemar test showed that GP approach outperforms MDA to the problem of corporate bankruptcy prediction.