Bankruptcy Prediction: Dynamic Geometric Genetic Programming (DGGP) Approach

In this paper, a new dynamic geometric genetic programming (dggp) technique is applied to empirical analysis of financial ratios and bankruptcy prediction. financial ratios are indeed desirable for prediction of corporate bankruptcy and identification of firms‘ impending failure for investors, creditors, borrowing firms, and governments. by the time, several methods have been attempted in the use of financial ratios on predicting bankruptcy but some of them suffer from underlying shortcomings. recently, genetic programming (gp) has received great attention in academic and empirical fields of solving high complex problems. the paper proposes the use of dynamic risk space measure (drs) on bankruptcy prediction utilized with genetic programming technique. the paper provides the evidence of the extent to which changes in values of this index are associated with changes in each values axis and how this may alter our economic interpretation of changes in the patterns and direction of risk. results of dynamic geometric genetic programming (dggp) classification methodology is compared with common and transformed ratios. results confirm the better accuracy which genetic classification tree achieved (overall 95.14% accuracy rate) using transformed ratios approach while original ratios model achieved only 88.85% accuracy rate.