Earticle

초록

영어

This study proposes a machine learning approach to understand how post-earnings-announcement drift (PEAD) works. We analyze when PEAD, combined with other factors, becomes more pronounced. To accommodate diverse variables and more complex specifications, two tree-based machine learning approaches including eXtreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM) are used to examine the relationship between PEAD and 89 variables. The long-short portfolio produced by LightGBM model reports 2.1 times higher returns than the portfolio’s returns, based on the conventional measure of earnings surprise. The model enhances the economic and statistical significance of the long-short portfolio returns. SHapley Additive exPlanations (SHAP) analysis determines feature importance and shows that liquidity, firm size, profitability ratios, share turnover, net trading flows by retail investors, and earnings surprises, play an important role in the prediction of PEAD.

목차

Abstract
Ⅰ. Introduction
II. Related Literature
1. Firm characteristics and PEAD
2. Firm characteristics and PEAD in Korea
3. Empirical Asset Pricing Using Machine Learning
Ⅲ. Data and Methodology
1. Data
2. Tree-based Machine Learning Algorithms
3. SHAP
Ⅳ. Results
V. Conclusion
References
Appendix A: Linear Regression Model
Appendix B: Characteristics of Sorted Portfolios Based on XGBoostModel

키워드

저자

• Dojoon Park [ School of Business, Yonsei University ]
• Jihoon Jung [ Graduate School of Information, Yonsei University ]
• Zoonky Lee [ Graduate School of Information, Yonsei University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  재무연구 [Asian Review of Financial Research]

• 간기

  계간

• pISSN

  1229-0351

• eISSN

  2713-6531

• 수록기간

  1988~2026

• 등재여부

  KCI 등재,SCOPUS

• 십진분류

  KDC 325 DDC 330