Research on the forecast ability of long short-term memory neural network model

Abstract

The stock market is usually regarded as a barometer of the economy, while the stock index can reflect the ups and downs, as well as trend changes of the stock market, to a certain extent. In recent years, the long short-term memory neural network model (LSTM model) has been widely used in the forecasting of stock prices due to its effectiveness. Nonetheless, few studies have focused on the forecasting ability of the LSTM model based on stock-index prices, with the effectiveness of this field still needing to be further explored. Against this background, this paper first constructs and designs the LSTM model of deep learning. Secondly, through the Min-Max normalization method to the data of three kinds of China A-share stock market indexes collected by Python, this paper carries out algorithm training for the LSTM model. Furthermore, based on the cleaned data, this paper conducts an empirical analysis of the price forecasting ability of the LSTM model, thus testing the accuracy of the LSTM model forecasting through the difference between the predicted and the true price curves. In closing, the paper draws relevant conclusions and puts forward targeted recommendations for improvement. Regarding research significance, the greatest contribution of this paper is to improve the stock-index price forecasting system and the research related to the defect system of the LSTM model.