A Quantification Approach of Changes in Firms' Financial Situation Using Neural Networks for Predicting Bankruptcy

Abstract

For a very long time, bankruptcy models were considered ahistorical, as they were mostly based on ratios measured over a single year. However, time is an essential variable that explains a firm's ability to survive. It is precisely for these reasons that measures intended to represent firm history have been studied and progressively used to complement traditional explanatory variables using financial ratios or variation indicators of such ratios. Even if these measures are not totally useless, they failed to be widely used in the literature. This is the reason why we propose a method, called temporal financial pattern–based method (TPM) that makes it possible to efficiently represent a firm's history using a quantification process and use the result of this process to improve model accuracy. This method relies on an estimation of typical temporal financial patterns that govern changes in a firm's financial situation over time, using neural networks. The results demonstrate that TPM leads to better prediction accuracy than that achieved with traditional models.