Prediction of Stock Price Based on LSTM Neural Network

Abstract

This study, based on the demand for stock price prediction and the practical problems it faces, compared and analyzed a variety of neural network prediction methods, and finally chose LSTM (Long Short-Term Memory, LSTM) neural network. Then, through in-depth study on how to predict the stock price by the LSTM neural network optimized by MBGD algorithm, the feasibility of the method and the applicability of the model are analyzed, and finally the conclusion is drawn. It is found that historical information is very important to investors as the basis of investment decisions. Past studies have used opening and closing prices as key new predicators of financial markets, but extreme maxima and minima may provide additional information about future price behavior. Therefore, the index of three representative stocks in China's stock market are selected as the research objects, and the key data collected from them include the opening price, closing price, lowest price, highest price, date and daily trading volume. The results show that although LSTM neural network model has some limitations, such as the time lag of prediction, but with attention layer, it can predict stock prices. Its main principle is to discover the role of time series through analyzing the historical information of the stock market, and to deeply explore its internal rules through the selective memory advanced deep learning function of LSTM neural network model, so as to achieve the prediction of stock price trend.