指数成份股定期调整事件系列报告：2025年12月指数成份股调整预测及事件效应跟踪

• The report utilizes a random forest model to predict the impact of index constituent stock adjustments on individual stocks' excess returns. The model is designed to handle complex, multi-dimensional, and non-linear problems effectively[13][17][24] - The random forest model selects features based on the logic that passive index funds adjust stock weights following index constituent changes, impacting related stocks. Key features include changes in passive fund holdings, stock liquidity, company market capitalization, and stock price trends[13][15][17] - The construction process of the random forest model involves training on historical data to predict excess returns for stocks affected by index adjustments. The model uses feature selection to enhance generalization ability and focuses on short-term impacts post-announcement[13][17][24] - The evaluation of the random forest model indicates its effectiveness in distinguishing the impact of index adjustments on stocks, particularly in sample-out tests. It successfully identifies stocks with significant excess returns or reduced negative effects[13][17][24] - The backtesting results show that stocks added to the CSI 300 index achieved an average excess return of 2.53% within 10 days post-announcement, while stocks added to the CSI 500 index achieved an average excess return of 1.01% in the same period[17][23][24] - Detailed group performance for stocks added to the CSI 300 index shows excess returns of 2.11% (group_1) and 1.48% (group_5) within 10 days, with a mean return of 2.53%. For the CSI 500 index, group_1 achieved 2.29%, group_5 achieved 0.88%, and the mean return was 1.01% within 10 days[23] - For stocks removed from the indices, the model shows reduced negative effects. CSI 300 stocks in group_1 achieved 1.44% within 10 days, while group_5 showed -0.80%, with a mean return of -0.25%. CSI 500 stocks in group_1 achieved 0.28%, group_5 achieved 0.48%, and the mean return was -0.11% within 10 days[31]