Quantitative Stock Selection Research Based on Deep Forest with Adaptive GBDT-RGF Cascade Layer
WANG Wenxuan
LI Lu
Abstract：The base learner gradient boosting tree(GBDT)and regularized greedy forest(RGF)are used to replace the ran-dom forest and completely random forest in the cascade layer of the deep forest allows dynamic iterative optimization of global param-eters.Meanwhile,an adaptive structure is introduced in the cascade layer to update the input data of the next layer by weighting the classification results of the previous layer according to the correct rate,and an adaptive GBDT-RGF cascade.The adaptive GB-DT-RGF cascade deep forest model is established.The model can enhance the correct features and weaken the incorrect features in the previous layer during the cascade layer training to improve the classification accuracy of the model.In addition,for the stock ups and downs affected by multi-day trend,the stock factor data before the prediction date is restructured and weighted,and the weight-ed data is used to predict the stock ups and downs.Experiments show that the multi-factor quantitative stock selection model with adaptive GBDT-RGF cascading layer depth forest,which alleviates the problem of stock prediction lag,achieves an annualized re-turn of 36.55%in the CSI 300 stock pool from January 2020 to June 2022,with a cumulative return of up to 164%,exceeding the re-turns of models such as deep forest and random forest.
Keywords：regularized greedy forestgradient boosting treefactor weightingenhanced adaptive cascade layermulti-fac-tor stock selection
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：8( 3319-3325,3391 )