HYBRID GENETIC PROGRAMMING-BASED SEARCH ALGORITHMS FOR ENTERPRISE BANKRUPTCY PREDICTION

Bankruptcy is an extremely significant worldwide problem that affects the economic well-being of all countries. The high social costs incurred by various stakeholders associated with bankrupt firms imply the need to search for better theoretical understanding and prediction quality. The main objective of this paper is to apply genetic programming with orthogonal least squares (GP/OLS) and with simulated annealing (GP/SA) algorithms to build models for bankruptcy prediction. Utilizing the hybrid GP/OLS and GP/SA techniques, generalized relationships are obtained to classify samples of 136 bankrupt and nonbankrupt Iranian corporations based on financial ratios. Another important contribution of this paper is to identify the effective predictive financial ratios based on an extensive bankruptcy prediction literature review and a sequential feature selection (SFS) analysis. A comparative study on the classification accuracy of the GP/OLS- and GP/SA-based models is also conducted. The observed agreement between the predictions and the actual values indicates that the proposed models effectively estimate any enterprise with regard to the aspect of bankruptcy. According to the results, the proposed GP/SA model has better performance than the GP/OLS model in bankruptcy prediction.