# Market-Dependent Communication in Multi-Agent Alpha Generation

arXiv: https://arxiv.org/abs/2511.13614 · HTML: https://mlsanity.com/paper/2511.13614/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use.
- Builds on 13 of its 16 references that are in the map.
- First posted on arXiv 17 Nov 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, 13 of 13)

- [Multi-agent Reinforcement Learning in Sequential Social Dilemmas](https://mlsanity.com/paper/1702.03037/) (2017)
- [TradingAgents: Multi-Agents LLM Financial Trading Framework](https://mlsanity.com/paper/2412.20138/) (2024)
- [A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist](https://mlsanity.com/paper/2402.18485/) (2024)
- [AlphaAgent: LLM-Driven Alpha Mining with Regularized Exploration to Counteract Alpha Decay](https://mlsanity.com/paper/2502.16789/) (2025)
- [QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model](https://mlsanity.com/paper/2402.03755/) (2024)
- [Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?](https://mlsanity.com/paper/2505.07078/) (2025)
- [MarketSenseAI 2.0: Enhancing Stock Analysis through LLM Agents](https://mlsanity.com/paper/2502.00415/) (2025)
- [MAPS: Multi-agent Reinforcement Learning-based Portfolio Management System](https://mlsanity.com/paper/2007.05402/) (2020)
- [Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment](https://mlsanity.com/paper/2402.09746/) (2024)
- [MSPM: A Modularized and Scalable Multi-Agent Reinforcement Learning-based System for Financial Portfolio Management](https://mlsanity.com/paper/2102.03502/) (2021)
- [ContestTrade: A Multi-Agent Trading System Based on Internal Contest Mechanism](https://mlsanity.com/paper/2508.00554/) (2025)
- [Sentiment trading with large language models](https://mlsanity.com/paper/2412.19245/) (2024)
- [CORE: Measuring Multi-Agent LLM Interaction Quality under Game-Theoretic Pressures](https://mlsanity.com/paper/2508.11915/) (2025)

## Abstract (arXiv)

Multi-strategy hedge funds face a fundamental organizational choice: should analysts generating trading strategies communicate, and if so, how? We investigate this using 5-agent LLM-based trading systems across 450 experiments spanning 21 months, comparing five organizational structures from isolated baseline to collaborative and competitive conversation. We show that communication improves performance, but optimal communication design depends on market characteristics. Competitive conversation excels in volatile technology stocks, while collaborative conversation dominates stable general stocks. Finance stocks resist all communication interventions. Surprisingly, all structures, including isolated agents, converge to similar strategy alignments, challenging assumptions that transparency causes harmful diversity loss. Performance differences stem from behavioral mechanisms: competitive agents focus on stock-level allocation while collaborative agents develop technical frameworks. Conversation quality scores show zero correlation with returns. These findings demonstrate that optimal communication design must match market volatility characteristics, and sophisticated discussions don't guarantee better performance.