Collective Investment Algorithms (CoinAlgs) are increasingly popular systems that deploy shared trading strategies for investor communities. Their goal is to democratize sophisticated -- often AI-based -- investing tools. We identify and demonstrate a fundamental profitability-fairness tradeoff in CoinAlgs that we call the CoinAlg Bind: CoinAlgs cannot ensure economic fairness without losing profit to arbitrage. We present a formal model of CoinAlgs, with definitions of privacy (incomplete algorithm disclosure) and economic fairness (value extraction by an adversarial insider). We prove two complementary results that together demonstrate the CoinAlg Bind. First, privacy in a CoinAlg is a precondition for insider attacks on economic fairness. Conversely, in a game-theoretic model, lack of privacy, i.e., transparency, enables arbitrageurs to erode the profitability of a CoinAlg. Using data from Uniswap, a decentralized exchange, we empirically study both sides of the CoinAlg Bind. We quantify the impact of arbitrage against transparent CoinAlgs. We show the risks posed by a private CoinAlg: Even low-bandwidth covert-channel information leakage enables unfair value extraction.

翻译：集体投资算法（CoinAlgs）是日益流行的系统，为投资者社区部署共享交易策略，其目标在于使复杂（通常基于人工智能的）投资工具民主化。我们发现并论证了CoinAlgs中一个根本性的盈利性与公平性权衡，称之为CoinAlg困境：若要保持经济公平性，CoinAlgs将因套利行为而损失利润。本文提出CoinAlgs的形式化模型，定义了隐私性（算法不完全披露）与经济公平性（对抗性内部人的价值提取）。我们证明了两组互补的结果，共同揭示了CoinAlg困境。首先，CoinAlg的隐私性是内部人攻击经济公平性的前提条件。反之，在博弈论模型中，缺乏隐私性（即透明度）会使套利者侵蚀CoinAlg的盈利能力。基于去中心化交易所Uniswap的实际数据，我们对CoinAlg困境的双重表现进行了实证研究：量化了透明CoinAlgs面临的套利影响，并揭示了私有CoinAlg的风险——即使低带宽隐蔽信道的信息泄露也会导致不公平的价值提取。