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Market-Dependent Communication in Multi-Agent Alpha Generation

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  • 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.

Suggested Citation

  • Jerick Shi & Burton Hollifield, 2025. "Market-Dependent Communication in Multi-Agent Alpha Generation," Papers 2511.13614, arXiv.org.
    • Handle: RePEc:arx:papers:2511.13614
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    References listed on IDEAS

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    1. Weixian Waylon Li & Hyeonjun Kim & Mihai Cucuringu & Tiejun Ma, 2025. "Can LLM-based Financial Investing Strategies Outperform the Market in Long Run?," Papers 2505.07078, arXiv.org, revised Nov 2025.
    2. Saizhuo Wang & Hang Yuan & Lionel M. Ni & Jian Guo, 2024. "QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model," Papers 2402.03755, arXiv.org.
    3. Kirtac, Kemal & Germano, Guido, 2024. "Sentiment trading with large language models," Finance Research Letters, Elsevier, vol. 62(PB).
    4. Goldstein, Itay & Xiong, Yan & Yang, Liyan, 2025. "Information sharing in financial markets," Journal of Financial Economics, Elsevier, vol. 163(C).
    5. Hang Yuan & Saizhuo Wang & Jian Guo, 2024. "Alpha-GPT 2.0: Human-in-the-Loop AI for Quantitative Investment," Papers 2402.09746, arXiv.org.
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