A Comprehensive Review of Reinforcement Learning Augmented by Large Language Models for Portfolio Management

Abstract

This paper presents a comprehensive review of reinforcement learning (RL) frameworks augmented by large language models (LLMs) for portfolio management. By examining existing literature and state-of-the-art methodologies, thereviewidentifiesthechallengesfacedinfinancialdecision-makingandexploressolutions provided by the integration of LLMs with RL systems.Key areas of focus include real-time sentiment analysis, transaction cost optimization,andstrategicportfolioallocation.Thisre- viewhighlightscomparativeperformancemetrics,challenges,andopportunitiesforimprovement in leveraging LLMs for RL-based financial strategies.Furthermore, future directions areproposedtorefinethesehybridsystemsfor broader market applications.

Introduction

This paper explores a novel portfolio management approach that combines Reinforcement Learning (RL) with Large Language Models (LLMs) to address the limitations of traditional, static investment strategies in volatile and complex financial markets.

1. Motivation and Problem Statement

Traditional portfolio management:

• Relies heavily on historical price data and fixed models

• Fails to adapt to real-time market changes or include qualitative data (e.g., news, sentiment)

• Suffers from high transaction costs and suboptimal adaptability

The integration of RL and LLMs:

• Enables adaptive learning from dynamic environments

• Uses unstructured textual data (news, social media) to improve decision-making

• Aims to optimize returns while minimizing costs

2. Literature Review Highlights

• RL in Finance: Jiang et al. proposed a deep RL framework that incorporates transaction costs.

• Asset Modeling: Yang et al. used normalizing flows for improved price distribution modeling.

• Cost-sensitive RL: Zhang et al. introduced policies to balance profitability and transaction costs.

• LLM for Sentiment: Zhang et al. also created Instruct-FinGPT to convert financial text into sentiment scores, feeding RL agents qualitative signals.

3. Proposed Framework

A hybrid system combining data-driven RL and language model-based sentiment analysis:

Key Components:

1. Data Collection: Aggregates real-time financial data and textual sentiment sources (news, social media).

2. Sentiment Analysis: Uses LLMs (e.g., FinBERT) to extract positive/neutral/negative sentiment from text.

3. Feature Engineering: Merges sentiment with quantitative indicators (e.g., volatility, moving averages).

4. RL Agent: Uses algorithms like Proximal Policy Optimization (PPO) to learn optimal trading actions, balancing profits and cost minimization.

5. Reward Function: Penalizes excessive trading while rewarding profitable, efficient decisions.

4. Advantages and Challenges

Benefits:

• Real-time adaptability to market changes

• Qualitative insight integration via sentiment

• Efficient trading strategies with cost sensitivity

• Potential for better returns than static models

Challenges:

• High computational demands from training RL and LLMs

• Bias in sentiment data if sources are skewed or limited

• Scalability issues for multi-asset or cross-market portfolios

• Lack of explainability, which hinders trust and adoption by human managers

5. Future Directions

• Better preprocessing to reduce sentiment bias

• Modular RL models tailored for different asset classes

• Multi-objective optimization (e.g., return, risk, cost)

• Explainable AI (XAI) tools to improve transparency and user trust

Conclusion

This review highlights the transformative po- tential of RL-LLM integration in revolutioniz- ing portfolio management practices.The hybrid framework enables real-time, sentiment- driven decision-making while addressing traditional limitations such as static models and high transaction costs.By bridging quantitative financial data with qualitative insights from sentiment analysis, this approach represents a paradigm shift toward adaptive, data- driven strategies. Future research directions should focus on optimizing computational efficiency to make such systems accessible to a broader range of users.Expanding the framework’s applicabilitytoglobalmarketsandmulti-assetportfolios is another critical step.Additionally, addressing biases in sentiment analysis and ensuring the scalability of RL models are necessary for broader adoption.Emphasizing explainability inRL-LLMsystemswillfurtherenhancetrustandusabilityforfinancialprofessionals. By addressing these challenges, the integrationofRLandLLMshasthepotentialto set a new benchmark for innovation in financial decision-making, paving the way for robust, adaptable, and intelligent portfolio management systems.

References

[1] Z.Jiang, D.Xu, andJ.Liang, “Adeeprein- forcement learning framework for the finan- cial portfolio management problem,” arXivpreprintarXiv:1706.10059,2017. [2] M.Yang,X.Zheng,Q.Liang,B.Han,andM. Zhu, “A smart trader for portfolio man- agement based on normalizing flows,” in Proceedings of the IJCAIConference on Ar- tificial Intelligence, 2022. [3] Y. Zhang, P. Zhao, Q. Wu, B. Li, J. Huang, andM.Tan,“Cost-sensitiveportfolioselec- tionviadeepreinforcementlearning,”IEEE Transactions on Knowledge and Data Engi- neering, vol. 34, no. 1, pp. 236–248, 2020. [4] B.Zhang,H.B.Yang,andX.-Y.Liu,“Instruct-fingpt:Financial sentiment analysis by instruction tuningofgeneral-purposelargelanguage models,” arXiv preprint, vol. arXiv:2306.12659, 2023. [Online]. Avail- able:https://arxiv.org/abs/2306.12659

Copyright

Copyright © 2025 Dr. R. A. Jamadar, Shraddha Pawaskar, Megha Pawar, Shruti Pitlellu, Avanti Shinde. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.