Toward Expert Investment Teams: A Multi-Agent LLM System with Fine-Grained Trading Tasks

The advancement of large language models (LLMs) has accelerated the development of autonomous financial trading systems. While mainstream approaches deploy multi-agent systems with hierarchical decision architectures, they often rely on coarse-grained instructions that underspecify the analytical procedures, leading to degraded inference quality and reduced transparency. We propose a structured decision architecture that explicitly decomposes investment analysis into fine-grained, domain-informed tasks assigned to specialized inference modules, rather than providing abstract role-level 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, validated through bootstrap confidence intervals, multiple-testing corrections, and subperiod stability analysis, demonstrate 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 semantic alignment between analytical outputs and downstream decision layers is a critical driver of system performance, consistent with a structured regularization interpretation. Moreover, we conduct standard portfolio optimization, exploiting low correlation with the stock index and the variance of each system’s output, achieving superior risk-adjusted performance. These findings contribute to the design of structured inference architectures and task decomposition strategies for LLM-based financial decision systems.