On the performance of learning machines for bankruptcy detection

Predicting the financial health of companies is a problem of great importance to various stakeholders in the increasingly globalized economy. We apply several learning machines methods to the problem of bankruptcy prediction of private companies. Financial data obtained from Diana, a database containing 780,000 financial statements of French companies, are used to perform experiments. Classification accuracy is evaluated with respect to artificial neural networks, linear genetic programming and support vector machines. We analyze both type I (bankrupted companies misclassified as healthy) and type II (healthy companies misclassified as bankrupted) errors on three datasets containing balanced and unbalanced class distribution. Linear genetic programming has the best accuracy in the balanced data while support vector machines is more stable for the unbalanced dataset. Our results, though preliminary in nature, demonstrate the tremendous potential of using learning machines in solving important economics problems such as predicting bankruptcy with accuracy