Proceedings of the 3rd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2024)

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A Survey of Deep Reinforcement Learning in Financial Markets

Authors
Ying Yu1, *
1Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, 200240, China
*Corresponding author. Email: yuying_mel@sina.com
Corresponding Author
Ying Yu
Available Online 7 May 2024.
DOI
10.2991/978-94-6463-419-8_24How to use a DOI?
Keywords
Reinforcement learning; stock price prediction; financial forecasting; sentiment analysis; deep learning; machine learning; artificial intelligence
Abstract

This paper surveys the application of reinforcement learning (RL) in stock price prediction, highlighting its potential and limitations. We explore how RL can be used to optimize trading strategies, manage investment risks, find arbitrage opportunities, and predict trends. The review classifies research objects and methods based on data frequency (high/non-high) and target (forecast/trading strategy). We analyze various asset classes (stocks, forex, etc.) and models (RL, neural networks, LSTMs) employed in previous works. Key findings suggest that RL offers advantages over traditional models by adapting to complex market dynamics, and that incorporating sentiment analysis can further enhance its effectiveness. We identify promising avenues for future research, including hybrid models, deeper sentiment integration, and improved risk management. Overall, the paper concludes that RL holds significant promise for transforming financial forecasting, leading to more accurate and adaptable decision-making tools.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 3rd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2024)
Series
Atlantis Highlights in Computer Sciences
Publication Date
7 May 2024
ISBN
10.2991/978-94-6463-419-8_24
ISSN
2589-4900
DOI
10.2991/978-94-6463-419-8_24How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ying Yu
PY  - 2024
DA  - 2024/05/07
TI  - A Survey of Deep Reinforcement Learning in Financial Markets
BT  - Proceedings of the 3rd International Academic Conference on Blockchain, Information Technology and Smart Finance (ICBIS 2024)
PB  - Atlantis Press
SP  - 188
EP  - 194
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-419-8_24
DO  - 10.2991/978-94-6463-419-8_24
ID  - Yu2024
ER  -