Chinese stock forecasting based on machine learning

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 effectiveness of different forecasting models such as the LSTM model and some well-known time series models. The experimental 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.