Computational Intelligent Techniques for Financial Distress Detection

Abstract

In this paper we apply several computational intelligence techniques to the problem of bankruptcy prediction of medium-sized private companies. Financial data was obtained from Diana, a large database containing financial statements of French companies. Classification accuracy is evaluated for Linear Genetic Programs (LGPs), Classification and Regression Tress (CART), TreeNet, and Random Forests, Multilayer Perceptron (using Back Propogation), Hidden Layer Learning Vector Quantization and several gradient descent methods, conjugate gradient methods, the Levenberg-Marquardt algorithm (LM), the Resilient Backpropogation Algorithm (Rprop), and One Step Secant Method. We analyze 2 datasets, one is balanced and the other unbalanced. TreeNet has the best performance accuracy on unbalanced dataset and LGPs performs the best on balanced dataset. Scaled Conjugate Gradient performs the best among the neural network training functions used for the balanced dataset; and Resilient Back Propagation performs the best among the training functions used for the unbalanced dataset. Our results demonstrate the great potential of using computational intelligent techniques, as an alternative to discriminant analysis, in addressing important economics problems such as bankruptcy prediction.

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