Design and Application of Quantitative Investment Strategies for Boosting Algorithm Fusion Models under Stacking Framework
The financial market is changing rapidly,and quantitative investment strategies need to be adjusted and optimized in a timely manner.The fusion model can dynamically adjust the weights of the model and the way of combination according to the market changes to achieve adaptive adjustment and optimization.In this paper,three different fusion two-layer Stacking models are constructed based on three different Boosting class algorithms,namely LightGBM,Adaboost,and XGBoost,and empirical analyses of stock picking backtesting are carried out on CSI 300 constituent stocks to compare and select the most suitable base learning model and the best fusion effect of the secondary model.The empirical results show that the three different fusion models outperform the single-algorithm models in stock market prediction,with the best performance being the fusion model where the base learners are the XGBoost and LightGBM algorithms,and the AdaBoost algorithm is used as the secondary learner.When the number of holdings is 20,the average annualized income is 13.57％,the Shape ratio is 1.23,and the maximum retracement is 0.48.In addition,the results of this backtest show that the fusion model has better adaptability and effectiveness in times of high market volatility.This study can provide investors with a new way of thinking about investment,and also provides some insights into how to promote the use of fusion models in financial practice.
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