HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization
It addresses portfolio optimization for financial traders by providing a scalable method that integrates sentiment signals, though it appears incremental in combining existing techniques.
This paper tackles financial portfolio optimization by integrating lightweight LLMs with hierarchical reinforcement learning to combine sentiment analysis from news with market data, achieving a 26% annualized return and a Sharpe ratio of 1.2, outperforming 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.