Multimodal Deep Reinforcement Learning for Portfolio Optimization

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Summary

This research paper proposes a reinforcement learning (RL) framework for portfolio optimization using multimodal data, including historical stock prices, sentiment analysis, and topic embeddings from news articles. The authors aim to enhance the state space representation by integrating financial sentiment data from SEC filings and news headlines and refining the reward function to better align with portfolio performance metrics.

Highlights

• The authors propose a deep reinforcement learning framework for portfolio optimization using multimodal data.
• The framework integrates historical stock prices, sentiment analysis, and topic embeddings from news articles.
• The authors use a novel sentiment embedding function to extract sentiment scores from news headlines.
• The framework is compared to traditional portfolio optimization techniques and advanced strategies.
• The results show that the proposed approach outperforms standard benchmarks, especially when utilizing combined data sources under profit-based reward functions.
• The authors also discuss the challenges of incorporating transaction costs into an RL model.
• The paper explores the use of news sentiment indicators, including shock and trends, and applies multiple learning-to-rank algorithms to construct an automated trading system.

Key Insights

• The proposed RL framework demonstrates the potential of using multimodal data for portfolio optimization, highlighting the importance of incorporating alternative data sources, such as sentiment analysis and topic embeddings, to improve portfolio performance.
• The use of a novel sentiment embedding function allows for the extraction of meaningful sentiment scores from news headlines, which can be used to inform portfolio decisions.
• The results of the study emphasize the importance of selecting an appropriate reward function, with the profit-based reward function leading to superior performance compared to the Differential Sharpe Ratio.
• The authors' discussion of transaction costs highlights the need for careful consideration of these costs when developing RL models for portfolio optimization.
• The exploration of news sentiment indicators and learning-to-rank algorithms demonstrates the potential for automated trading systems to leverage news data for improved portfolio performance.
• The study's findings suggest that the use of combined data sources, including historical prices, SEC filings, and news data, can lead to improved portfolio performance.
• The authors' approach to comparing the proposed framework to traditional and advanced strategies provides a comprehensive evaluation of the framework's performance.

Mindmap

Citation

Nawathe, S., Panguluri, R., Zhang, J., & Venkatesh, S. (2024). Multimodal Deep Reinforcement Learning for Portfolio Optimization (Version 1). arXiv. https://doi.org/10.48550/ARXIV.2412.17293