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

arXiv: https://arxiv.org/abs/2411.04788 · HTML: https://mlsanity.com/paper/2411.04788/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 7 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [A Survey on Large Language Model based Autonomous Agents](https://mlsanity.com/paper/2308.11432/) (2023)
- [BloombergGPT: A Large Language Model for Finance](https://mlsanity.com/paper/2303.17564/) (2023)
- [Personal LLM Agents: Insights and Survey about the Capability, Efficiency and Security](https://mlsanity.com/paper/2401.05459/) (2024)
- [AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environments](https://mlsanity.com/paper/2405.07960/) (2024)
- [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024)
- [FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design](https://mlsanity.com/paper/2311.13743/) (2023)
- [Large Language Models for Robotics: A Survey](https://mlsanity.com/paper/2311.07226/) (2023)
- [Revolutionizing Finance with LLMs: An Overview of Applications and Insights](https://mlsanity.com/paper/2401.11641/) (2024)
- [InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning](https://mlsanity.com/paper/2309.13064/) (2023)
- [When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments](https://mlsanity.com/paper/2407.18957/) (2024)

## Built on by (most cited first, 14 of 14)

- [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://mlsanity.com/paper/2504.19678/) (2025)
- [A Survey of LLM-Driven AI Agent Communication: Protocols, Security Risks, and Defense Countermeasures](https://mlsanity.com/paper/2506.19676/) (2025)
- [A Survey on Agent Workflow -- Status and Future](https://mlsanity.com/paper/2508.01186/) (2025)
- [Position: Stop Acting Like Language Model Agents Are Normal Agents](https://mlsanity.com/paper/2502.10420/) (2025)
- [Integrating Large Language Models in Financial Investments and Market Analysis: A Survey](https://mlsanity.com/paper/2507.01990/) (2025)
- [FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs](https://mlsanity.com/paper/2412.10823/) (2024)
- [A Review of Large Language Models for Stock Price Forecasting from a Hedge-Fund Perspective](https://mlsanity.com/paper/2605.05211/) (2026)
- [Synthesizing Behaviorally-Grounded Reasoning Chains: A Data-Generation Framework for Personal Finance LLMs](https://mlsanity.com/paper/2509.14180/) (2025)
- [Signal or Noise in Multi-Agent LLM-based Stock Recommendations?](https://mlsanity.com/paper/2604.17327/) (2026)
- [Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysis](https://mlsanity.com/paper/2605.18770/) (2026)
- [A cybersecurity AI agent selection and decision support framework](https://mlsanity.com/paper/2510.01751/) (2025)
- [FinRL-X: An AI-Native Modular Infrastructure for Quantitative Trading](https://mlsanity.com/paper/2603.21330/) (2026)
- [HabitatAgent: An End-to-End Multi-Agent System for Housing Consultation](https://mlsanity.com/paper/2604.00556/) (2026)
- [An Empirical Study of Automating Agent Evaluation](https://mlsanity.com/paper/2605.11378/) (2026)

## Abstract (arXiv)

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.