Multimodal Deep Reinforcement Learning for Portfolio Optimization

TL;DR

This paper introduces a multimodal deep reinforcement learning framework that integrates stock prices, sentiment analysis, and news embeddings to improve portfolio optimization for SP100 stocks, demonstrating superior performance over traditional methods.

Contribution

The paper presents a novel RL approach combining multimodal financial data and advanced feature extraction to enhance portfolio optimization strategies.

Findings

Outperforms traditional portfolio optimization techniques

Utilizing combined data sources improves portfolio performance

Deep RL agent effectively leverages multimodal information

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.