FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification
This addresses portfolio management for financial investors by combining sentiment and market data, though it appears incremental as an integration of existing methods.
The paper tackles portfolio optimization by integrating LLM-based sentiment analysis with traditional market data through a hierarchical reinforcement learning framework, achieving 26% annualized returns and a Sharpe ratio of 1.2 that outperforms benchmarks.
This paper presents a novel hierarchical framework for portfolio optimization, integrating lightweight Large Language Models (LLMs) with Deep Reinforcement Learning (DRL) to combine sentiment signals from financial news with traditional market indicators. Our three-tier architecture employs base RL agents to process hybrid data, meta-agents to aggregate their decisions, and a super-agent to merge decisions based on market data and sentiment analysis. Evaluated on data from 2018 to 2024, after training on 2000-2017, the framework achieves a 26% annualized return and a Sharpe ratio of 1.2, outperforming equal-weighted and S&P 500 benchmarks. Key contributions include scalable cross-modal integration, a hierarchical RL structure for enhanced stability, and open-source reproducibility.