Portfolio Optimization in Dynamic Markets: Reinforcement Learning for Investment

Authors

  • Paramita Sarkar Assistant Professor, JIS University, Kolkata, West Bengal
  • Pruthiranjan Dwibedi Research Scholar, School of Social, Financial and Human Sciences, KIIT Deemed to be University, Bhubaneswar
  • Shailesh Shivaji Deore Associate Professor, Department of Computer Engineering, SSVPS B S DEORE College of Engineering Dhule Maharashtra
  • Tukaram Gawali Assistant Professor, Government College of Engineering, Jalgaon, Maharashtra state, Maharashtra
  • Mayuri Diwakar Kulkarni Assistant Professor, Department of Computer Engineering, SVKM's Institute of Technology, Dhule, Maharashtra
  • Animesh Saha Research Scholar, Department of Commerce, Assam University, Silchar-788011, Assam, India.

Keywords:

Reinforcement Learning, Optimization, Machine Learning, Risk Management, Dynamic Market

Abstract

In today's volatile and ever-changing financial markets, optimizing portfolio allocation is a formidable job. This study offers a novel strategy for optimizing a portfolio in volatile markets by drawing on techniques from the field of Reinforcement Learning (RL). As a result of being slow to adapt to ever-changing market conditions, traditional investment strategies sometimes generate worse returns and expose their owners to more risk. In contrast, the RL-based method offers a dynamic and adaptable answer to this age-old problem. The proposed model uses RL to learn and improve its tactics over time in order to maximize returns while effectively limiting risk. Because RL is so malleable, the portfolio can instantly respond to fluctuations in the market, grabbing opportunities and avoiding setbacks. We conduct extensive tests using historical market data to evaluate our RL-based portfolio optimization approach and to compare it to conventional investment strategies. Our research proves that even in volatile markets, RL can produce superior risk-adjusted returns. We also shed light on the practical implementation of RL in portfolio management by providing insights into the key factors influencing its effectiveness. This research is an important first step in rethinking investment strategies for fluid markets. To improve investment outcomes and risk management, we give investors a robust framework for portfolio optimization that can survive and even thrive in volatile market environments. We accomplish this by making use of RL's tremendous potential.

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Published

29.01.2024

How to Cite

Sarkar, P. ., Dwibedi, P. ., Deore, S. S. ., Gawali, T. ., Kulkarni, M. D. ., & Saha, A. . (2024). Portfolio Optimization in Dynamic Markets: Reinforcement Learning for Investment. International Journal of Intelligent Systems and Applications in Engineering, 12(13s), 386–395. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4605

Issue

Vol. 12 No. 13s (2024)

Section

Research Article

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