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

Quick Overview

Researchers from Japan Digital Design and the University of Oxford developed a multi-agent LLM system that strictly mimics the daily operations of a real institutional investment team, achieving superior performance metrics like a Sharpe Ratio of 1.91 compared to the baseline of 1.38, primarily by using fine-grained instructions and rigorous testing methodologies like ablation studies.

Key Points: The multi-agent LLM system was designed to mimic the stratified structure of an expert investment team, featuring a Level 1 agent for information aggregation and a Level 2 'Adjustment Layer' agent. The system uses GPT-4 as the underlying model and was trained using 252 days of raw closing prices from Japan's largest cap stocks (September 2023 to November 2025). The fine-grained setup, which included specific instructions like setting the temperature parameter to one, yielded a superior Sharpe Ratio of 1.91, significantly outperforming the baseline of 1.38. The Level 1 agent focuses solely on information aggregation (technical and macro data), while the Level 3 Portfolio Manager agent synthesizes outputs from lower levels to make the final decision. A critical finding was that removing the Level 1 technical agent caused performance to plummet, proving that specialized agents are necessary and that simply throwing more compute at a large model is insufficient. The system successfully avoided look-ahead bias by relying only on data available up to the simulation date and explicitly excluded data from future events during training.

Context: This video discusses a research paper from collaborators at Japan Digital Design and the University of Oxford detailing a novel multi-agent Large Language Model (LLM) system designed to replicate the complex, hierarchical decision-making process of expert investment teams. The core innovation lies in structuring the agents to handle specific, fine-grained tasks—from data aggregation to final investment decisions—rather than relying on broad, generalized LLM outputs.