Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research
This work addresses the need for better investment decision-making in finance by demonstrating incremental improvements through multi-agent collaboration over single-agent approaches.
The paper tackled the problem of single-agent AI systems in financial analysis by proposing a multi-agent collaboration system, which outperformed traditional models with improved accuracy, efficiency, and adaptability in analyzing 30 Dow Jones companies' 2023 SEC 10-K forms.
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.