# FinArena: A Human-Agent Collaboration Framework for Financial Market Analysis and Forecasting

arXiv: https://arxiv.org/abs/2503.02692 · HTML: https://mlsanity.com/paper/2503.02692/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Mar 2025.

## Built on by (most cited first, 8 of 8)

- [Fin-R1: A Large Language Model for Financial Reasoning through Reinforcement Learning](https://mlsanity.com/paper/2503.16252/) (2025)
- [A Survey of Reasoning and Agentic Systems in Time Series with Large Language Models](https://mlsanity.com/paper/2509.11575/) (2025)
- [A Call for Collaborative Intelligence: Why Human-Agent Systems Should Precede AI Autonomy](https://mlsanity.com/paper/2506.09420/) (2025)
- [Integrating Large Language Models in Financial Investments and Market Analysis: A Survey](https://mlsanity.com/paper/2507.01990/) (2025)
- [Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents](https://mlsanity.com/paper/2510.17491/) (2025)
- [A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective](https://mlsanity.com/paper/2605.05211/) (2026)
- [HAS-Bench: Evaluating LLM-Based Human-Agent Systems under Configurable Human Participation](https://mlsanity.com/paper/2607.04329/) (2026)
- [SMARTFinRAG: Interactive Modularized Financial RAG Benchmark](https://mlsanity.com/paper/2504.18024/) (2025)

## Abstract (arXiv)

To improve stock trend predictions and support personalized investment decisions, this paper proposes FinArena, a novel Human-Agent collaboration framework. Inspired by the mixture of experts (MoE) approach, FinArena combines multimodal financial data analysis with user interaction. The human module features an interactive interface that captures individual risk preferences, allowing personalized investment strategies. The machine module utilizes a Large Language Model-based (LLM-based) multi-agent system to integrate diverse data sources, such as stock prices, news articles, and financial statements. To address hallucinations in LLMs, FinArena employs the adaptive Retrieval-Augmented Generative (RAG) method for processing unstructured news data. Finally, a universal expert agent makes investment decisions based on the features extracted from multimodal data and investors' individual risk preferences. Extensive experiments show that FinArena surpasses both traditional and state-of-the-art benchmarks in stock trend prediction and yields promising results in trading simulations across various risk profiles. These findings highlight FinArena's potential to enhance investment outcomes by aligning strategic insights with personalized risk considerations.