A Machine Learning Approach to Stock Market Prediction Based on Multi-Source Information

Multiple interrelated elements hinder precise stock market predictions. Conventional machine learning models may insufficiently represent the intricacies of financial markets since they rely on data from a single source and presume exact instance-level classification. Our research presents an innovative method for stock market forecasting called Multi-Source Multiple Instance Learning (MS-MIL). This methodology allows the model to integrate information from many sources, including historical stock prices, financial news, social media sentiment, and macroeconomic indicators, all organized into aggregated data instances (bags). The model adeptly tackles restricted supervision and intrinsic uncertainty in financial data by consolidating each source into separate sets of examples and using a multiple instance learning approach. Our MS-MIL system employs sophisticated feature extraction and attention techniques to combine numerical and textual data for the training of discriminative representations. The experimental results demonstrate that the suggested strategy exceeds traditional learning models in forecasting stock movements and market trends. This technique exhibits promise as a tool for analysts and investors to make informed judgments owing to its superior resilience, flexibility, and interpretability.