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

Paper: arXiv 2411.04788

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.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper focuses on practical AI agent implementation and testing on real SEC 10-K data, with empirical results like prediction accuracy metrics, but contains minimal advanced mathematical formalism or derivations.

Research Flowchart

  flowchart TD
  A["Research Goal: Enhance Investment Analysis<br>by Optimizing AI-Agent Collaboration"] --> B{"Data Collection & Selection"}
  B --> C["Input: 30 Dow Jones 10-K Forms (2023)"]
  C --> D["Multi-Agent Collaboration System"]
  D --> E{"Sub-Optimal Combination Strategy"}
  E --> F["Computational Task Groups"]
  F --> G["Fundamentals Analysis"]
  F --> H["Market Sentiment Analysis"]
  F --> I["Risk Analysis"]
  G --> J{"Dynamic Adaptation to Market Conditions"}
  H --> J
  I --> J
  J --> K["Outcomes: Multi-Agent System Outperforms Single-Agent Models"]
  K --> L["Key Findings:<br>1. Improved Accuracy<br>2. Enhanced Efficiency<br>3. Greater Adaptability"]
  style A fill:#e1f5e1
  style K fill:#f0e1ff
  style L fill:#fff2cc

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4 of 134 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.