AlphaAgents: Large Language Model based Multi-Agents for Equity Portfolio Constructions

TL;DR

This paper explores the use of large language model-based multi-agent systems for equity portfolio construction, demonstrating their potential in stock selection and portfolio management through comprehensive analysis and benchmarking.

Contribution

It introduces a role-based multi-agent framework utilizing LLMs for equity research, providing new insights into their performance and practical challenges in portfolio construction.

Findings

Multi-agent systems outperform benchmarks in stock selection.

Role-based agents adapt to different risk tolerances effectively.

Framework reveals strengths and limitations of AI-driven portfolio management.

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.