Paper 1: Sentiment-Driven Forecasting LSTM Neural Networks for Stock Prediction-Case of China Bank Sector
Abstract: This study explores the predictive analysis of public sentiment in China's financial market, focusing on the banking sector, through the application of machine learning techniques. Specifically, it utilizes the Baidu Index and Long Short-Term Memory (LSTM) networks. The Baidu Index, akin to China's version of Google Trends, serves as a sentiment barometer, while LSTM networks excel in analyzing sequential data, making them apt for stock price forecasting. Our model integrates sentiment indices from Baidu with historical stock data of significant Chinese banks, aiming to unveil how digital sentiment influences stock price movements. The model's forecasting prowess is rigorously evaluated using metrics such as R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and confusion matrices, the latter being instrumental in assessing the model's capability in correctly predicting stock up or down movements. Our findings predominantly showcase superior prediction performance of the sentiment-based LSTM model compared to a standard LSTM model. However, effectiveness varies across different banks, indicating that sentiment integration enhances prediction capabilities, yet individual stock characteristics significantly contribute to the prediction accuracy. This inquiry not only underscores the importance of integrating public sentiment in financial forecasting models but also provides a pioneering framework for leveraging digital sentiment in financial markets. Through this endeavor, we offer a robust analytical tool for investors, policymakers, and financial institutions, aiding in better navigation through the intricate financial market dynamics, thereby potentially leading to more informed decision-making in the digital age.
Keywords: Machine learning; LSTM; sentiment; forecasting; banking sector