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

2025/08/15 by Zhao, Tianjiao, Lyu, Jingrao, Jones, Stokes +3 · 2 citations
#Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Economics and business #Statistical Finance (q-fin.ST)

paper · doi:10.48550/arxiv.2508.11152

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.

Citations

• MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents
• FinVerse: An Autonomous Agent System for Versatile Financial Analysis
• A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
• A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications
• Retrieval-Augmented Generation for Large Language Models: A Survey
• Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents
• API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs
• A Survey on In-context Learning
• Reinforcement Learning: An Introduction

Cited by

• QuantEvolve: Automating Quantitative Strategy Discovery through Multi-Agent Evolutionary Framework
• Identifying Financial Risk Information Using RAG with a Contrastive Insight

Related

• TradingAgents: Multi-Agents LLM Financial Trading Framework
• The Evolution of Alpha in Finance Harnessing Human Insight and LLM Agents
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments
• Hierarchical AI Multi-Agent Fundamental Investing: Evidence from China's A-Share Market
• Enhancing Investment Analysis: Optimizing AI-Agent Collaboration in Financial Research