Multi-modal Prediction System: Hybrid Deep Learning, FinBERT and PPO Optimization

Stock market fluctuations are difficult to predict due to the complex interaction of history and fluctuations in public sentiment. Most existing trading models rely primarily on "hard" quantitative metrics and therefore may overlook significant information about stocks and the need for automation in managing trades [3]. To solve this problem, we have developed a comprehensive multi-modal trading framework that combines together expertise from deep learning, natural language processing (NLP), and reinforcement learning in order to allow the integration of all aspects of trading into one complete trading platform. The architecture we used employs two main types of networks the Long Short Term Memory (LSTM) networks and XGBoost. The LSTM will help us to monitor the time-based movements while the XGBoost will provide us with the best performance for working with our tabular data. We will also include FinBERT (a new model trained with financial data) to extract useful sentiment from both news articles and social media channels [3]. The combination of these three different methods will create a more intelligent Proximal Policy Optimization (PPO) trading algorithm. Our results demonstrate that the synergy between sentiment and ensemble modeling significantly reduce our RMSE (root mean square error) and significantly increase cumulative returns over a traditional approach. This study represents a full end-to-end research project that provides a fully deployable capability to achieve accurate forecast models and automated, intelligent financial actions.