Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research

TL;DR

This paper introduces a multi-agent AI collaboration system for financial analysis that outperforms single-agent models by dynamically adapting to market conditions and leveraging diverse analytical strengths.

Contribution

It presents a novel multi-agent system with configurable collaboration structures that enhances decision-making in financial research, a significant advancement over existing single-agent approaches.

Findings

Multi-agent system improves accuracy over single-agent models

Dynamic adaptation enhances performance across different market scenarios

System effectively analyzes fundamentals, sentiment, and risk in financial data

Abstract

In recent years, the application of generative artificial intelligence (GenAI) in financial analysis and investment decision-making has gained significant attention. However, most existing approaches rely on single-agent systems, which fail to fully utilize the collaborative potential of multiple AI agents. In this paper, we propose a novel multi-agent collaboration system designed to enhance decision-making in financial investment research. The system incorporates agent groups with both configurable group sizes and collaboration structures to leverage the strengths of each agent group type. By utilizing a sub-optimal combination strategy, the system dynamically adapts to varying market conditions and investment scenarios, optimizing performance across different tasks. We focus on three sub-tasks: fundamentals, market sentiment, and risk analysis, by analyzing the 2023 SEC 10-K forms of 30 companies listed on the Dow Jones Index. Our findings reveal significant performance variations based on the configurations of AI agents for different tasks. The results demonstrate that our multi-agent collaboration system outperforms traditional single-agent models, offering improved accuracy, efficiency, and adaptability in complex financial environments. This study highlights the potential of multi-agent systems in transforming financial analysis and investment decision-making by integrating diverse analytical perspectives.