Measuring corporate failure risk: Does long short-term memory perform better in all markets?

Abstract

Recently, various corporate failure prediction models that use machine learning techniques have received considerable attention. In particular, using a sequence of a company's historical information, rather than just the most recent information, yields better predictive performance by adopting recurrent neural networks (RNNs) and long short-term memory (LSTM) algorithms in the United States market. Similarly, we evaluate whether these results hold in emerging market contexts using listed companies in Korea. We also compare the logistic regression, random forest, RNN, LSTM, and an ensemble model combining these four techniques. The random forest model with recent information outperforms the other models, indicating that corporate failure prediction models for immature markets, unlike those for developed markets, might have to focus more on recent information rather than on the historical sequence of corporate performance.