Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Data Mining Using SAS Applications
Data Mining Using SAS Applications
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters
Expert Systems with Applications: An International Journal
A comparison of classification accuracy of four genetic programming-evolved intelligent structures
Information Sciences: an International Journal
A portfolio optimization model using Genetic Network Programming with control nodes
Expert Systems with Applications: An International Journal
Genetic programming for credit scoring: The case of Egyptian public sector banks
Expert Systems with Applications: An International Journal
Constructing portfolio investment strategy based on time adapting genetic network programming
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A model of portfolio optimization using time adapting genetic network programming
Computers and Operations Research
Expert Systems with Applications: An International Journal
Genetic relation algorithm with guided mutation for the large-scale portfolio optimization
Expert Systems with Applications: An International Journal
Using partial least squares and support vector machines for bankruptcy prediction
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
International Journal of Intelligent Systems in Accounting and Finance Management
Hi-index | 12.06 |
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.