A Transparent Single Financial Asset Trading Framework via Reinforcement Learning

This paper introduces a novel single financial asset trading framework leveraging reinforcement learning to integrate fundamental, technical, and sentiment analysis for stock investment. By harmonizing these diverse analytical methods with advanced computational techniques, the study aims to forge a robust, adaptable investment strategy capable of delivering precise market trend predictions and efficient asset allocation. The framework’s uniqueness lies in its single-asset focus, simplifying the investment process while ensuring depth and clarity in analysis. The research meticulously evaluates the potential of reinforcement learning in finance by utilizing data from various sources, including financial statements and media visibility, alongside sophisticated models like CNNs and LSTMs. The evaluation against traditional benchmarks and sensitivity analysis under different market conditions highlights the model’s effectiveness in enhancing risk-adjusted returns and investment decision transparency. By integrating SHAP values for model interpretability, this study advances the field of quantitative finance by providing a transparent investment decision-making process and lays the groundwork for future research in developing more refined and comprehensive trading algorithms.