재무연구 제 권 호 (2022년 11월)
Asian Review of Financial Research, Vol., No..
pp.609~628
pp.609~628
Prediction of Corporate Bankruptcy with Machine Learning
Haein Lee Department of Applied Artificial Intelligence Sungkyunkwan University
Byunghoon Yu Department of FinTech Sungkyunkwan University Seoul, Korea
Jang Hyun Kim Department of Applied Artificial Intelligence Sungkyunkwan University
Heungju Park Department of FinTech SKK Business School Sungkyunkwan University
This study examines the predictability of various machine learning and deep learning models in corporate default forecasts. Using a sample of U.S. corporate defaults over the period of 1963-2020, we find Ensemble classifier and Bi-LSTM classifier forecast the corporate bankruptcy better than other models and the predictability of the Ensemble classifier is more stable in year-to-year variability. Further, machine learning models outperform deep learning models in high yield grade samples, while deep learning models performs better than machine learning models in investment grade samples.
Haein Lee
Byunghoon Yu
Jang Hyun Kim
Heungju Park
This study examines the predictability of various machine learning and deep learning models in corporate default forecasts. Using a sample of U.S. corporate defaults over the period of 1963-2020, we find Ensemble classifier and Bi-LSTM classifier forecast the corporate bankruptcy better than other models and the predictability of the Ensemble classifier is more stable in year-to-year variability. Further, machine learning models outperform deep learning models in high yield grade samples, while deep learning models performs better than machine learning models in investment grade samples.
