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[2602.23330] Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks
Summary
This article presents a multi-agent LLM framework for financial trading, emphasizing fine-grained task decomposition to enhance decision-making and performance in investment analysis.
Why It Matters
As financial markets increasingly rely on AI, understanding how to optimize trading systems with LLMs is crucial. This research highlights the importance of detailed task breakdowns for improving trading outcomes, which could influence future AI applications in finance.
Key Takeaways
- Fine-grained task decomposition in LLMs enhances trading performance.
- The proposed framework outperforms traditional coarse-grained approaches.
- Alignment between analytical outputs and decision preferences is key to success.
- Utilizing diverse data sources improves risk-adjusted returns.
- The findings can guide the design of LLM-based trading systems.
Computer Science > Artificial Intelligence arXiv:2602.23330 (cs) [Submitted on 26 Feb 2026] Title:Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks Authors:Kunihiro Miyazaki, Takanobu Kawahara, Stephen Roberts, Stefan Zohren View a PDF of the paper titled Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks, by Kunihiro Miyazaki and 2 other authors View PDF HTML (experimental) Abstract:The advancement of large language models (LLMs) has accelerated the development of autonomous financial trading systems. While mainstream approaches deploy multi-agent systems mimicking analyst and manager roles, they often rely on abstract instructions that overlook the intricacies of real-world workflows, which can lead to degraded inference performance and less transparent decision-making. Therefore, we propose a multi-agent LLM trading framework that explicitly decomposes investment analysis into fine-grained tasks, rather than providing coarse-grained instructions. We evaluate the proposed framework using Japanese stock data, including prices, financial statements, news, and macro information, under a leakage-controlled backtesting setting. Experimental results show that fine-grained task decomposition significantly improves risk-adjusted returns compared to conventional coarse-grained designs. Crucially, further analysis of intermediate agent outputs suggests that alignment between analytical outputs and downstrea...
