# FinMarBa: A Market-Informed Dataset for Financial Sentiment Classification

arXiv: https://arxiv.org/abs/2507.22932 · HTML: https://mlsanity.com/paper/2507.22932/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 17 references that are in the map.
- First posted on arXiv 24 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [FinBERT: Financial Sentiment Analysis with Pre-trained Language Models](https://mlsanity.com/paper/1908.10063/) (2019)
- [Good Debt or Bad Debt: Detecting Semantic Orientations in Economic Texts](https://mlsanity.com/paper/1307.5336/) (2013)
- [Multilingual Twitter Sentiment Classification: The Role of Human Annotators](https://mlsanity.com/paper/1602.07563/) (2016)
- [Generating insights about financial asks from Reddit posts and user interactions](https://mlsanity.com/paper/2403.04308/) (2024)
- [Stress index strategy enhanced with financial news sentiment analysis for the equity markets](https://mlsanity.com/paper/2404.00012/) (2024)

## Built on by (most cited first, 1 of 1)

- [Frontier Financial Judgement: Can agents tell what might move a stock?](https://mlsanity.com/paper/2607.20645/) (2026)

## 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.