Earticle

초록

영어

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.

목차

Abstract
1. Introduction
2. Data
3. Empirical Method
3.3. Machine Learning for Classification
3.4. Deep Learning for Classification
3.5 Optimal Hyperparameters and Cross Validation
4. Empirical Results
4.1. Predictability Test with Whole Data Set
4.2. Predictability Test with Year-split Data Set
4.3. Predictability Test with Rating-split Data Set
5. Conclusion
References

키워드

저자

• 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 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국재무학회 학술대회

• 간기

  부정기

• 수록기간

  2006~2024

• 십진분류

  KDC 325 DDC 330