Published in Data Science and Engineering (DSE) Record 2023 Vol. 4 No. 1 pp. 1-11
Abstract
In the financial market analysis field, machine learning techniques for stock price prediction have garnered considerable interest. This study investigates the effectiveness of Long Short-Term Memory (LSTM) models in predicting stock prices for growth stocks and the CSI 300 index in the Chinese A-share market. The study also explores the influence of various technical in-dicators of the LSTM model and models on forecasting accuracy. The exper-imental results demonstrate that the LSTM model is the most effective in predicting stock prices in the A-share market, while other algorithms such as WMA and ARIMA are not as successful in forecasting long-term stock market data. This study proposes some modifications further to enhance the accuracy and dependability of the prediction model.