Abstract
Since the “high stock dividend” of A-share companies in China often leads to the short-term stock price increase, this phenomenon’s prediction has been widely concerned by academia and industry. In this study, a new multi-layer stacking ensemble algorithm is proposed. Unlike the classic stacking ensemble algorithm that focused on the differentiation of base models, this paper used the equal weight comprehensive feature evaluation method to select features before predicting the base model and used a genetic algorithm to match the optimal feature subset for each base model. After the base model’s output prediction, the LightGBM (LGB) model was added to the algorithm as a secondary information extraction layer. Finally, the algorithm inputs the extracted information into the Logistic Regression (LR) model to complete the prediction of the “high stock dividend” phenomenon. Using the A-share market data from 2010 to 2019 for simulation and evaluation, the proposed model improves the AUC (Area Under Curve) and F1 score by 0.173 and 0.303, respectively, compared to the baseline model. The prediction results shed light on event-driven investment strategies.
Subject
General Physics and Astronomy
Reference48 articles.
1. Dividend Policy, Growth, and the Valuation of Shares
2. Eastmoney Pagehttp://finance.eastmoney.com/a/202012021721604784.html
3. Research on the Phenomenon of “High Stock Dividend” in Chinese Stock Market;Li;Manag. World,2014
4. The Chinese Stock Dividend Puzzle
5. High Transfer, Accounting Conservatism and the Scale of Large Shareholders’ Reduction;Lai;East China Econ. Manag.,2020
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献