Market-Dependent Communication in Multi-Agent Alpha Generation

ArXiv ID: 2511.13614 “View on arXiv”

Authors: Jerick Shi, Burton Hollifield

Abstract

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.

Keywords: Multi-strategy hedge funds, LLM-based trading systems, Organizational structures, Communication design, Market volatility, Equity

Complexity vs Empirical Score

• Math Complexity: 3.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper’s mathematical content is limited to alpha expression formulas and basic statistical measures, with no complex derivations or advanced theory, placing math complexity at the lower end. Empirical rigor is very high due to 450 experiments over 21 months, three market universes, multiple organizational structures, and detailed performance metrics (returns, Sharpe ratios, strategy correlations), making it highly backtest-ready.

  flowchart TD
    A["Research Goal: Optimal Communication<br>in Multi-Agent Trading"] --> B["Methodology: 5 LLM Agents<br>5 Org Structures × 21 Months"]
    B --> C["Data: 450 Experiments<br>Across 3 Stock Types"]
    C --> D["Computation: Simulate Trading<br>with Varying Conversation Modes"]
    D --> E{"Findings: Market-Dependent Outcomes"}
    E --> F["Vol Tech Stocks:<br>Competitive Wins"]
    E --> G["Stable General Stocks:<br>Collaborative Wins"]
    E --> H["Finance Stocks:<br>No Communication Benefit"]