Bankruptcy Prediction Using Stacked Auto-Encoders

Abstract

Bankruptcy prediction is considered as one of the vital topics in finance and accounting. The purpose of predicting bankruptcy is to build a predictive model that combines several econometrics parameters, which allow evaluating the firm financial status either bankrupt or non-bankrupt. In this field, various machine learning algorithms such as decision tree, support vector machine, and artificial neural network have been applied to predict bankruptcy. However, deep learning algorithms are experiencing a resurgence of interest. To this end, we propose a novel deep learning-based approach which includes both feature extraction and classification phase into one model for predicting bankruptcy of financial firms. Our approach combines Stacked Auto-Encoders (SAE) with softmax classifier. In the first stage, the stacked auto-encoders are employed to extract the best features from the training dataset. Second, a softmax classification layer is trained to predict the class label. We evaluate our proposed approach on the base of Polish and Darden datasets. The obtained results confirm the efficiency of the SAE with softmax classifier compared to other existing works to accurately predict corporate bankruptcy.