Multi-factor quantification strategy of LGBM based on post-earnings announcement drift

In the era of intensified volatility of domestic and international capital markets, exploring effective factors and market information to construct appropriate investment portfolio strategies is of great significance to control risks and obtain stable and sustainable excess returns. In this study we select the performance reports of A-share listed stocks from the first quarter of 2018 to 2022 as research objects. Taking 1 to 12 weeks after the company's earnings announcement as the time window, we examine the post-earnings-announcement drift (PEAD) of stock prices, selecting the expected excess earnings factor, a proxy variable of market anomaly, and other 5 related market anomaly factors. The effective factors are screened and tested by information coefficient (IC), information ratio (IR) and double sorting methods. Considering that the quantitative stock selection is a classification task with low data volume, low frequency and high validity of eigenvalues, a multi-factor quantitative strategy based on a lightweight gradient boosting tree is used to construct a portfolio to predict stock returns. This approach is compared with traditional quantitative strategies (simple scoring method, single factor model based on expected surplus, IC value weighted multi-factor stock selection model) and quantitative strategies based on other machine learning models such as support vector regression (SVR), artificial neural network (ANN) and extreme gradient boosting (XGBoost). Empirical results show that in forecasting stock excess returns based on the PEAD effect in the A-share market, the portfolio constructed by the light gradient boosting machine (LGBM) multi-factor quantitative strategy achieves an average annual return of 21.633% in the long-short combination portfolio, exceeding the benchmark annualized return of 20.184%. The comprehensive analysis of various indicators reflect that the investment portfolio constructed by LGBM multi-factor quantitative strategy has excellent performance in the A-share market and more show significant improvement compared to other quantitative strategies while maintaining stability, which can better control portfolio risks and achieve higher excess returns.