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·Peer-reviewed·ISSN (Online): 2169-0103·ISSN (Print): 0252-2667
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The Journal of Information and Optimization Sciences (JIOS) is a world leading journal publishing high quality, rigorously peer-reviewed original research in all mathematically-oriented theoretical and applied topics in information sciences, optimization sciences and related areas since 1980. Subjects include but are not limited to:
• Information Sciences
• Optimization Sciences
• Control Theory
• Operational Research
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Issues up to 2022 co-published with and available at:
An optimization strategy for portfolio management leveraging deep reinforcement learning for automated stock trading
Gurunath T. Chavangt.chavan@gmail.comDepartment of Information Technology Vishwakarma Institute of TechnologyPune, Maharashtra, 411037, IndiaView full profile →
, *Santosh H. LavateCorresponding authorlavate.santosh@gmail.comDepartment of Electronics and Telecommunication Engineering AISSMS College of EngineeringPune, Maharashtra, 411001, IndiaView full profile →
, Pradip Ram Selokarselokarpr@rknec.eduDepartment of Electronics and Communication Engineering Ramdeobaba UniversityNagpur, Maharashtra, 440013, IndiaView full profile →
, Deepti Raverkardeepti.amb@gmail.comDepartment of Computer Engineering JSPM’s Rajarshi Shahu College of Engineering (RSCOE)Pune, Maharashtra, 411033, IndiaView full profile →
, Aniket Prakashrao Munshianiketpmunshi@gmail.comDepartment of Electrical Engineering Yeshwantrao Chavan College of EngineeringNagpur, Maharashtra, 441110, IndiaView full profile →
, Kriti Kusumkriti.ksm05@gmail.comSymbiosis School of Planning Architecture and Design Nagpur Campus Symbiosis International UniversityPune, Maharashtra, 440008, IndiaView full profile →
* Corresponding author · click or hover a name for details
Traditional strategies for managing portfolios often rely on rigid models and people’s gut feelings, which might not work as well when the market is changing and unclear. Instead, DRL seems like a good way to make decisions that are adaptable because it learns from past data and interacts with the market environment. A system that combines DRL algorithms with financial data is what we’re suggesting as the best way to make trade and stock allocation decisions. By changing stock weights based on market signs and past success, the DRL robot learns how to get the most total benefits. We show that our method works by doing a lot of back testing on old market data and comparing it to other strategies that have been used before. In terms of risk-adjusted returns and ability to handle market instability, our DRL-based portfolio management plan does better than standard approaches. We also look at how different hyper-parameters and design choices affect how well the DRL agent works. Overall, our study adds to the growing amount of research on using machine learning in financial markets and gives practitioners who want to improve portfolio management through automation and flexible decision-making useful information.
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