The Machines are Coming Non-parametric methods and bankruptcy prediction - An artificial neural network approach

Abstract

Prediction of corporates bankruptcies is a topic that has gained more importance in the last two decades. Improvement in data accessibility makes the topic of bankruptcy prediction models a widely studied area. This study looks at bankruptcy prediction from a non-parametric perspective, with a focus on artificial neural networks (ANNs). Inspired by the classical work by Altman (1968) this study models bankruptcies with classification techniques. Five different models - ANN, CART, k- NN, LDA and QDA are applied to Swedish, German and French firm level datasets. The study findings suggests the ANN method outperforms other methods with 86.49% prediction accuracy and struggles to separate the smallest companies in the dataset from the defaulted ones. It is also shown that an increase in number of hidden layers from 10 to 100 results in an increase of 1% in prediction accuracy but the effect is non-linear.