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ISSN Approved Journal No: 2320-2882 | Impact factor: 7.97 | ESTD Year: 2013
Call For Paper - Volume 14 | Issue 4 | Month- April 2026
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Published Paper Details:
Paper Title
A Systematic Literature Review: Stock Market Price Prediction Using Reinforcement Learning
Authors
Shantanu Dipak Rajmane, Dr. Rupali Bagate, Dr Aparna Joshi
Keywords
deep reinforcement learning, stock market prediction, Q-learning, Deep Q-Network, financial forecasting, systematic review, sentiment analysis
Abstract
This paper presents a systematic literature review of reinforcement learning (RL) techniques, particularly deep reinforcement learning (DRL), applied to stock market price prediction. Through comprehensive analysis of recent research, we find that advanced algorithms such as Q-Learning, Double DQN, and Dueling DQN, especially when combined with sentiment analysis from news and social media, create powerful frameworks that address financial markets' complexity. Our review indicates that DRL approaches significantly outperform traditional methods in the literature, resulting in more adaptive and dynamic solutions for stock market forecasting. This paper synthesizes current research findings and identifies promising directions for future work in this rapidly evolving field.
IJCRT's Publication Details
Unique Identification Number - IJCRTBL02023
Paper ID - 301034
Page Number(s) - 110-113
Pubished in - Volume 14 | Issue 2 | February 2026
DOI (Digital Object Identifier) - https://doi.org/10.56975/ijcrt.v14i2.301034
Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882
E-ISSN Number - 2320-2882
Cite this article
Shantanu Dipak Rajmane, Dr. Rupali Bagate, Dr Aparna Joshi, "A Systematic Literature Review: Stock Market Price Prediction Using Reinforcement Learning", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.14, Issue 2, pp.110-113, February 2026, Available at :http://www.ijcrt.org/papers/IJCRTBL02023.pdf
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Submit Manuscript Online Impact Factor: 7.97 Review Results : Within 02-03 Days Paper Publication : Within 02-03 Days
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ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
ISSN and 7.97 Impact Factor Details
ISSN: 2320-2882 Impact Factor: 7.97 and ISSN APPROVED Journal Starting Year (ESTD) : 2013
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