A-share Trend Prediction Based on Machine Learning and Sentiment Analysis

Abstract

This study addresses the predictive challenges in China’s A-share market, characterized by high retail investor participation and significant policy impacts. We introduce a novel predictive model that leverages both machine learning algorithms and sentiment analysis to forecast market trends. The research utilizes comprehensive datasets, including real-time A-share market data and sentiment-derived data from stock-related news, processed via advanced machine learning techniques like Random Forest and sentiment analysis tools. Our approach innovatively combines traditional technical indicators with sentiment scores to enhance the predictive accuracy of the model. The findings suggest that integrating sentiment analysis significantly improves the model’s performance, evidenced by enhanced prediction metrics such as Mean Absolute Error (MAE) and R-squared values, which compare favorably before and after incorporating sentiment data. This study not only contributes to the existing financial prediction literature by providing a hybrid methodological approach but also offers practical implications for investors and policymakers in navigating the volatile A-share market.