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Multimodal Deep Reinforcement Learning for Portfolio Optimization

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  • Sumit Nawathe
  • Ravi Panguluri
  • James Zhang
  • Sashwat Venkatesh

Abstract

We propose a reinforcement learning (RL) framework that leverages multimodal data including historical stock prices, sentiment analysis, and topic embeddings from news articles, to optimize trading strategies for SP100 stocks. Building upon recent advancements in financial reinforcement learning, we 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. Our methodology includes deep reinforcement learning with state tensors comprising price data, sentiment scores, and news embeddings, processed through advanced feature extraction models like CNNs and RNNs. By benchmarking against traditional portfolio optimization techniques and advanced strategies, we demonstrate the efficacy of our approach in delivering superior portfolio performance. Empirical results showcase the potential of our agent to outperform standard benchmarks, especially when utilizing combined data sources under profit-based reward functions.

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  • Sumit Nawathe & Ravi Panguluri & James Zhang & Sashwat Venkatesh, 2024. "Multimodal Deep Reinforcement Learning for Portfolio Optimization," Papers 2412.17293, arXiv.org.
    • Handle: RePEc:arx:papers:2412.17293
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    References listed on IDEAS

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    1. Yunan Ye & Hengzhi Pei & Boxin Wang & Pin-Yu Chen & Yada Zhu & Jun Xiao & Bo Li, 2020. "Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States," Papers 2002.05780, arXiv.org.
    2. Zhengyao Jiang & Dixing Xu & Jinjun Liang, 2017. "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem," Papers 1706.10059, arXiv.org, revised Jul 2017.
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