MASFIN: A Multi-Agent System for Decomposed Financial Reasoning and Forecasting

• URL: http://arxiv.org/abs/2512.21878v1
• Date: Fri, 26 Dec 2025 06:01:55 GMT
• Title: MASFIN: A Multi-Agent System for Decomposed Financial Reasoning and Forecasting
• Authors: Marc S. Montalvo, Hamed Yaghoobian,
• Abstract summary: We introduce MASFIN, a modular multi-agent framework that integrates structured financial metrics and unstructured news.<n>In an eight-week evaluation, MASFIN delivered a 7.33% cumulative return, outperforming the S&P 500, NASDAQ-100, and Dow Jones benchmarks in six of eight weeks.<n>These findings demonstrate the promise of bias-aware, generative AI frameworks for financial forecasting.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in large language models (LLMs) are transforming data-intensive domains, with finance representing a high-stakes environment where transparent and reproducible analysis of heterogeneous signals is essential. Traditional quantitative methods remain vulnerable to survivorship bias, while many AI-driven approaches struggle with signal integration, reproducibility, and computational efficiency. We introduce MASFIN, a modular multi-agent framework that integrates LLMs with structured financial metrics and unstructured news, while embedding explicit bias-mitigation protocols. The system leverages GPT-4.1-nano for reproducability and cost-efficient inference and generates weekly portfolios of 15-30 equities with allocation weights optimized for short-term performance. In an eight-week evaluation, MASFIN delivered a 7.33% cumulative return, outperforming the S&P 500, NASDAQ-100, and Dow Jones benchmarks in six of eight weeks, albeit with higher volatility. These findings demonstrate the promise of bias-aware, generative AI frameworks for financial forecasting and highlight opportunities for modular multi-agent design to advance practical, transparent, and reproducible approaches in quantitative finance.

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