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

arXiv: https://arxiv.org/abs/2508.11152 · HTML: https://mlsanity.com/paper/2508.11152/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 15 Aug 2025.

## Placed in

- LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/)

## Builds on (most cited first, 9 of 9)

- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [Retrieval-Augmented Generation for Large Language Models: A Survey](https://mlsanity.com/paper/2312.10997/) (2023)
- [A Survey on In-context Learning](https://mlsanity.com/paper/2301.00234/) (2022)
- [API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs](https://mlsanity.com/paper/2304.08244/) (2023)
- [A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications](https://mlsanity.com/paper/2402.07927/) (2024)
- [Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents](https://mlsanity.com/paper/2306.03314/) (2023)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [FinVerse: An Autonomous Agent System for Versatile Financial Analysis](https://mlsanity.com/paper/2406.06379/) (2024)

## Built on by (most cited first, 16 of 16)

- [Evaluating LLMs in Finance Requires Explicit Bias Consideration](https://mlsanity.com/paper/2602.14233/) (2026)
- [Alpha-R1: Alpha Screening with LLM Reasoning via Reinforcement Learning](https://mlsanity.com/paper/2512.23515/) (2025)
- [Toward Expert Investment Teams:A Multi-Agent LLM System with Fine-Grained Trading Tasks](https://mlsanity.com/paper/2602.23330/) (2026)
- [QuantEvolve: Automating Quantitative Strategy Discovery through Multi-Agent Evolutionary Framework](https://mlsanity.com/paper/2510.18569/) (2025)
- [Self-Improvement of Large Language Models: A Technical Overview and Future Outlook](https://mlsanity.com/paper/2603.25681/) (2026)
- [A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective](https://mlsanity.com/paper/2605.05211/) (2026)
- [Signal or Noise in Multi-Agent LLM-based Stock Recommendations?](https://mlsanity.com/paper/2604.17327/) (2026)
- [AgentSteerTTS: A Multi-Agent Closed-Loop Framework for Composite-Instruction Text-to-Speech](https://mlsanity.com/paper/2605.17583/) (2026)
- [DSA: Evidence-Aware LLM-Agent Orchestration for Multi-Market Stock Research](https://mlsanity.com/paper/2608.26990/) (2026)
- [Identifying Financial Risk Information Using RAG with a Contrastive Insight](https://mlsanity.com/paper/2510.03521/) (2025)
- [Beyond Agent Architecture: Execution Assumptions and Reproducibility in LLM-Based Trading Systems](https://mlsanity.com/paper/2606.08285/) (2026)
- [Can Agentic Trading Systems Pay for Their Own Intelligence?](https://mlsanity.com/paper/2607.10286/) (2026)
- [OpenPM: Auditable Point-in-Time Evaluation for LLM Portfolio-Management Agents](https://mlsanity.com/paper/2608.09988/) (2026)
- [From Numbers to Judgment: Specialist LLM Agents and Reinforcement Learning for European Listed Real Estate](https://mlsanity.com/paper/2608.11381/) (2026)
- [Poisoning Agentic Alpha: Adversarial Vulnerabilities Across Roles and Architectures in Multi-Agent Trading Systems](https://mlsanity.com/paper/2608.24069/) (2026)
- [Multi-Agent Debate for Explainable Trading: Reasoning, Consensus, and Performance in Simulated Markets](https://mlsanity.com/paper/2609.29701/) (2026)

## Abstract (arXiv)

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.