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《Journal of Hainan Normal University(Natural Science)》 2018-01
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The Application of GBDT Combination Model in Stock Forecasting

ZHANG Xiao;WEI Zengxin;YANG Tianshan;School of Mathematics and Information Science,Guangxi University;School of Finance and Economics,Nanning College for Vocational Technology;  
The article uses the combination of contribution degree and correlation analysis to extract the optimal technical index from the 244 most commonly used stock technical indices,and then uses gradient boosting decision tree(GBDT) to forecast the trend of the stock. Empirical analysis of the combination model consisting of the contribution degree,correlation analysis and GBDT algorithm(referred to as GBDT combination model) is made,and for the first time the GBDT algorithm is applied to predict the CSI 300 stock set. The prediction accuracy of the combined model composed of different algorithms is also compared and analyzed. The experimental results show that the GBDT combination model is superior to the linear regression combination model and random forest combination model in prediction accuracy.
【Fund】: 国家自然科学基金(11161003);; 中青年教师基础能力提升项目(2017KY1017)
【CateGory Index】: F832.51
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