Research on Corporate Bankruptcy Prediction Analysis Based on Financial and Non-Financial Information Using Deep Learning

Abstract

In the past, research related to corporate bankruptcy has primarily conducted empirical analyses through bankruptcy prediction models using financial ratios. However, with the advancement of ICT technology, there has been a growing trend in applying artificial intelligence. In this study, both traditional corporate bankruptcy prediction methodologies and machine learning and deep learning methodologies from the field of deep learning were applied to present the results of corporate bankruptcy prediction models and their predictive power. The dataset used included corporate characteristics, including financial ratios and non-financial information, as well as macroeconomic indicators to account for economic conditions. Five models, SVM, RF, DNN, CNN, and LSTM, were designated, and the model reliability and prediction accuracy for each model were analyzed. The LSTM model demonstrated superior performance and the highest prediction accuracy among the models. When comparing different approaches using only financial ratios (Set 1), using financial ratios and corporate characteristics together (Set 2), and incorporating financial ratios, corporate characteristics, and macroeconomic indicators (Set 3), which included all of these factors, consistently exhibited the highest model reliability and prediction accuracy.