Stock factors have characteristics such as richness and similarity,it is more difficult to get good results in trend prediction.To solve the problem,RF-MIC-PCA algorithm is proposed.Firstly,the GI of random forest(RF)is used to construct the importance scoring rules between factors and categories to eliminate low-score factors.Then,the maximal information coefficient(MIC)is used to construct a correlation evaluation method between factors,and integrated the principal components analysis(PCA)to reduce the redundancy of factors.Finally,the stock trend prediction algorithm based on RF-MIC-PCA is established by using the classification accuracy of the random forest prediction as a measure.To verify the effectiveness of the algorithm,10 representative stocks from the CSI 300 are selected for the experiment.The results show that the RF-MIC-PCA algorithm effectively improves the prediction performance of the algorithm while reducing the dimension of the data set by 20.45％.In addition,the trend prediction of the CSI 300 and SSE 50 indices improves the accuracy by 4.1％and 5.0％,which verify the universality of the algorithm and have certain practical value.
Stock trend predictionRandom forestMaximal information coefficientPrincipal component analysis
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