Plan Planning For The Forecast Of The Closing Price Of The Shanghai Stock Exchange Index

With the continuous improvement of living standards and the continuous development of the domestic financial market,people's investment methods are gradually diversified.In recent years,more and more investors start to get involved in the securities market,especially the stock market.However,due to the large scale,complex structure,highly fuzzy,non-linear and other characteristics of stock historical data,it brings great difficulties to the index prediction method based on historical data.The complex and changeable financial market has raised the requirements of investors for the ability to analyze the market.The traditional financial theory should not only be extended and applied to the market,but also be combined with cutting-edge science and technology.Big data,cloud computing,artificial intelligence and other technologies have been applied to stock price forecasting.In this study,firstly,we introduce the investor sentiment of behavioral finance,which reflect the irrational factors of investors in the securities market.We construct the emotional index which can measure the investor sentiment to a certain extent.We select five variables as proxy indicator of investor sentiment,that are A-share market turnover(TURN),A-share volume(VOL),consumer confidence index(CCI),A-share market active account number(ASA)and Baidu Index(BDI).And we construct the investor sentiment index(SENT)by referring to Baker and Wurgle's method of constructing BW sentiment index.Secondly,this paper introduces the NARX dynamic neural network forecasting model,and creatively adds the investor sentiment to the neural network model as an input to the neural network.Finally,this paper constructs the NARX dynamic neural network prediction model based on investor sentiment.Dynamic neural network based on the investor sentiment is the combination of the irrational factors of the investor sentiment and the neural network,which has strong adaptive learning ability.This model is applied to the forecast of the securities market price.The empirical results show that the dynamic accuracy of the dynamic neural network based on investor sentiment is better than that of the ELM extreme learning machine prediction model and the traditional linear ARIMA model.The results of this study and the corresponding calculation program can provide reasonable theoretical support and investment basis for investors to build a good investor strategy.It provides reference basis for index fund investors in the investment strategy of tracking index,so as to achieve the purpose of gaining more than the average return of the market.