Research On Stock Prediction And Stock Selection Method Based On Intelligent Algorithms

As an important part of the investment process,stock investment analysis is an important basis for successful stock investment.Investors often explore the law of market changes by analyzing a large number of stock indicator data(such as closing price,trading volume,etc.),and then make decisions in investment.Due to the limited ability of individuals to analyze complex data,how to use intelligent technology to empower investment analysis has become a mainstream research subjects in the field of stock investment.This thesis will conduct research on three important stages in the process of stock investment analysis based on machine learning and deep learning intelligent algorithms: index forecasting,stock selection,and individual stock forecasting.The main work is as follows:(1)Aiming at the difficulty of index forecasting caused by the aliasing about multi-cycle volatility of stock indicators,a method for predicting the rise and fall of sector indexes that integrates the multi-cycle volatility features of stock indicators is proposed(IMCV).The method firstly splits the aliased multi-cycle volatility in the stock indicators through Complete Ensemble Empirical Mode Decomposition with Adaptive Noise algorithm and obtains the multi-cycle volatility features corresponding to each stock indicator;then,a parallel multi-branch neural network model is designed,it integrates the multi-cycle volatility features of multiple stock indicators,and realizes the prediction of the rise and fall of the sector index.Through experiments on the three datasets of "Agriculture,Forestry,Animal Husbandry and Fisheries","Mining" and "Food and Beverage" of Shen Wan Primary Sector Indexes,it is proved that IMCV has better prediction performance of rising and falling categories than the baseline method;The general rules summed up in the parameters of the parallel multi-branch network can provide guidance for the relevant personnel to analyze the market.(2)Aiming at the high risk of the stock portfolio caused by the high degree correlation of individual stock,a risk-diversified stock selection method based on attribute association network clustering(AANC＿SS)is proposed.The method firstly extracts the kernel principal component features of stock indicators to build a stock attribute correlation network,which eliminates the redundancy of stock indicators and expresses the implicit connection relationship between individual stocks.By clustering stocks in attribute correlation network through the Louvain algorithm,the different clusters in the connection pattern of the stocks are obtained;finally,according to the clustering results diversely select stock,which reduces the risk in the stock portfolio.Experiments have shown that the stock selection method described in this thesis can achieve a higher return to risk ratio than the benchmark index and the compared method in the sideways market,the continuous rising market and the market with multiple state changes.(3)Aiming at the problem of insufficient representation and use of the co-movement relationship between stocks in individual stock price prediction,a stock price prediction method based on co-movement graph for stock return(CGSR)is proposed.This method builds the co-movement graph of stock return based on the results of the co-movement of individual stock prices,which avoids the problem of insufficient co-movement relationship representation caused by insufficient mining of the "cause" of the co-movement between stock prices.The Seq2 Seq model,based on the Diffusion Convolution Gate Recurrent Unit as the basic module,effectively integrates the co-movement features and temporal features of stock prices,and realizes the future price prediction of individual stocks in the co-movement graph.Experiments show that the CGSR method has a smaller error than the baseline method in the continuous multi-step stock price prediction;through visualization,it is found that the one-step stock price prediction curve of the CGSR method has a smaller lag to the real price curve.As mentioned above,the index forecasting method(IMCV)proposed in this thesis can obtain more accurate market trends,the proposed stock selection method(AANC＿SS)can make stock investment bear lower risks,and the proposed individual stock price forecasting method(CGSR)can obtain more accurate individual stock price trends.Combining the three methods can enable investors to accurately grasp the market environment,rationally allocate funds,and accurately grasp the timing of buying and selling during the investment process,so as to obtain higher returns while taking lower risks.