Implementing Reinforcement Learning in a Financial Decision Support System for Stock Market Investment

Authors- Ratnesh Kumar Sharma, Professor (Dr) Satya Singh

Abstract-The dynamic and complex nature of stock markets necessitates advanced tools for effective investment decision-making. Traditional methods often fall short in capturing market intricacies and adapting to rapid changes. This paper explores the implementation of reinforcement learning (RL) in a financial decision support system (FDSS) designed for stock market investment. Reinforcement learning, which allows agents to learn optimal strategies through trial and error, is particularly suited for the unpredictable environment of financial markets. We propose an RL-based model that leverages historical stock data and key financial indicators to develop and refine investment strategies. Our system employs advanced RL algorithms, including Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO), to maximize returns and manage risks. Through extensive testing on benchmark datasets, our model demonstrates superior performance compared to traditional investment strategies, achieving higher cumulative returns and better risk-adjusted outcomes. The results indicate that incorporating RL into financial decision-making processes can significantly enhance investment strategies, offering a promising avenue for future research and application in automated trading systems. This paper explores the application of reinforcement learning (RL) in developing a financial decision support system (FDSS) for stock market investment. Reinforcement learning, an area of machine learning where agents learn optimal actions through trial and error, offers significant potential in the dynamic and complex environment of stock markets. We propose an RL-based model that leverages historical stock data and financial indicators to optimize investment strategies. The system’s performance is evaluated against traditional investment strategies, demonstrating superior returns and risk management capabilities. This research highlights the benefits and challenges of implementing RL in financial decision-making and suggests directions for future enhancements.

DOI: /10.61463/ijset.vol.12.issue5.258