Paper: arXiv 2508.11152

Authors: Tianjiao Zhao, Jingrao Lyu, Stokes Jones, Harrison Garber, Stefano Pasquali, Dhagash Mehta

Abstract

The field of artificial intelligence (AI) agents is evolving rapidly, driven by the capabilities of Large Language Models (LLMs) to autonomously perform and refine tasks with human-like efficiency and adaptability. In this context, multi-agent collaboration has emerged as a promising approach, enabling multiple AI agents to work together to solve complex challenges. This study investigates the application of role-based multi-agent systems to support stock selection in equity research and portfolio management. We present a comprehensive analysis performed by a team of specialized agents and evaluate their stock-picking performance against established benchmarks under varying levels of risk tolerance. Furthermore, we examine the advantages and limitations of employing multi-agent frameworks in equity analysis, offering critical insights into their practical efficacy and implementation challenges.

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 relies primarily on LLM prompt engineering and system design rather than advanced mathematical derivations, resulting in low math complexity; however, it includes a systematic backtest against benchmarks with specific performance metrics and risk profiles, demonstrating substantial empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: LLM Multi-Agents for Equity Portfolio Construction"] --> B["Methodology: Role-Based Multi-Agent System"]
  B --> C["Inputs: Financial Data & Market News"]
  C --> D["Process: Collaborative Stock Selection"]
  D --> E["Process: Portfolio Construction vs Benchmarks"]
  E --> F["Outcomes: Stock-Picking Performance & Risk Analysis"]
  F --> G["Conclusion: Efficacy & Challenges of Multi-Agent Frameworks"]