Empirical Analysis Of Stock Predictions Based On LSTM

In 2019,many major events occurred in the A-share market,such as the opening of the Science and Technology Board,the opening of the Shanghai-London Stock Connect,and the landing of the reorganization of new regulations.China's stock market is on the path of change and is becoming more and more perfect.With advances in technology,large amounts of financial data are preserved,providing a solid data base for stock market analysis.With deep learning research,people are constantly exploring its application to the stock market.Recurrent neural network(RNN)is a neural network that introduces the concept of time series,and its proposal provides a new method for analyzing time series data.Long-short term neural network(LSTM)is an improved version of traditional recurrent neural network,because it introduces the concept of gate,which solves the problem of RNN gradient disappearance and explosion,and can also deal with the problem of long-term dependence of data.Compared with the traditional time series model,thanks to the internal nonlinear activation function,LSTM has some advantages in studying the problem of nonlinear sequence related data.This paper attempts to validate the applicability of LSTM to stock data analysis through the empirical analysis of both stock price prediction and rise and fall prediction.At the same time,it will analyze the problems of LSTM,that are prone to appear when fitting the model.In addition,it will provide suggestions about how to solve these problems.It is hoped to provide some valuable information for constructing the LSTM stock forecasting model.This paper selects the trading data,technical index and valuation index of CSI 300 in the past five years as the sample data.First,in the experiment of stock prediction,the closing index is modeled and predicted by LSTM from the perspective of single feature input and multi-feature input,and then the ARIMA model is used as the comparative model of closing index prediction to analyze the applicability of LSTM in stock price prediction.Secondly,in the experiment of rise and fall prediction,the LSTM model is also used to model the rise and fall of closing index from the angle of single feature input and multi-feature input.Through empirical research,both sets of experiments can illustrate the relatively good effect of the LSTM multi-featured input prediction model,proving the applicability of the LSTM multi-featured input model in stock prediction.In terms of closing index predictions,the LSTM multi-feature input model is about 100 points lower than the MSE of the ARIMA model and about 150 points lower than the MSE of the LSTM single-feature input model.In terms of the results of predicting the rise and fall of the closing index,the LSTM multi-featured input model is 23%more correct than the LSTM single-featured input model.Finally,this paper analyzes the problems of overfitting and instability of LSTM in stock prediction modeling and proposes specific solutions.