mlsanityData to 7 Oct 2026

HARLF: Hierarchical Reinforcement Learning and Lightweight LLM-Driven Sentiment Integration for Financial Portfolio Optimization

arXiv 2507.18560 ↗First posted 24 Jul 2025q-fin.PM cs.AIcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 12 of its 23 references that are in the map. First posted on arXiv 24 Jul 2025.

Builds on

12 of 23 references.

References · 12

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  3. 2015Continuous control with deep reinforcement learningcited by 3,279
  4. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  5. 2019FinBERT: Financial Sentiment Analysis with Pre-trained Language Modelscited by 378
  6. 2016Hierarchical Deep Reinforcement Learning: Integrating Temporal Abstraction and Intrinsic Motivationcited by 282
  7. 2023Large Language Models in Finance: A Surveycited by 223
  8. 2017A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problemcited by 123
  9. 2024L3iTC at the FinLLM Challenge Task: Quantization for Financial Text Classification & Summarizationcited by 4
  10. 2024'Finance Wizard' at the FinLLM Challenge Task: Financial Text Summarizationcited by 4
  11. 2024CatMemo at the FinLLM Challenge Task: Fine-Tuning Large Language Models using Data Fusion in Financial Applicationscited by 2
  12. 2024Optimizing Performance: How Compact Models Match or Exceed GPT's Classification Capabilities through Fine-Tuningcited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Agentic Trading: When LLM Agents Meet Financial Marketscited by 7
  2. 2026TrustTrade: Human-Inspired Selective Consensus Reduces Decision Uncertainty in LLM Trading Agentscited by 3