Financial Distress Prediction of Chinese Listed Companies Using the Combination of Optimization Model and Convolutional Neural Network

Abstract

In order to predict financial distress in 3424 Chinese listed companies, we incorporate a novel time windows optimization model into a convolutional neural network and use 576 financial/nonfinancial/macroindicators as the model input data. Our prediction accuracy can reach 94.5%, at least 2% higher than known classifiers (e.g., support vector machine, decision tree, logistic regression, neural network). In terms of AUC and the Kolmogorov-Smirnov statistic, our model also outperformed these classifiers. The introduction of the optimization model in our model can combine indicator information in different time windows, leading to the best prediction performance.