Algorithmic Intermediation and the International Transmission of U.S. Monetary Policy

Abstract

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.