AI 中文总结
研究算法和人工智能驱动的基金管理对美国货币政策国际传导的影响，构建宏观金融框架并检验，发现算法羊群行为在高波动时放大货币冲击后资金外流，政策应注重保持模型多样性。
AI 中文摘要
本文研究了算法和人工智能驱动的基金管理如何塑造美国货币政策向新兴市场的国际传导。认为不稳定的关键来源并非算法中介本身，而是各基金模型的相似性。算法依赖相似信号并产生相关误差时，压力时期交易相互强化，加剧资本流动反应；模型多样则误差相互抵消，算法投资者可稳定资金流。构建两区域宏观金融框架，用2000至2024年19个新兴市场股票投资组合资金流检验核心预测。证据表明，仅在高波动时期，算法羊群行为会放大美国货币冲击后的资金外流，而仅更快调整无此效果。结果意味着政策应关注保持模型多样性而非限制非银行中介规模。
英文摘要
This paper examines how algorithmic and AI-driven fund management shapes the international transmission of U.S. monetary policy to emerging markets. It argues that the key source of instability is not algorithmic intermediation itself, but the similarity of models across funds. When algorithms rely on similar signals and make correlated errors, their trades reinforce one another and intensify capital-flow responses during periods of stress. When models are diverse, errors offset each other and algorithmic investors can stabilize flows. The paper develops a two-region macro-financial framework and tests its central prediction using equity portfolio flows to nineteen emerging markets from 2000 to 2024. The evidence shows that algorithmic herding amplifies outflows after U.S. monetary shocks only in high-volatility regimes, while faster adjustment alone has no comparable effect. The results imply that policy should focus on preserving model diversity rather than limiting the size of non-bank intermediation.
