Analyzing the Impact of Algorithmic Trading on Market Efficiency: A Case Study of the European Equity Markets

Morgan Harris; Taylor White; Jordan Jones; Adrian Young

Authors

Morgan Harris PhD
Taylor White D.Sc
Jordan Jones Associate Professor
Adrian Young Professor

Keywords:

algorithmic trading, market efficiency, European equity markets, bid-ask spread, price discovery, high-frequency trading, volatility analysis, economic implications, regulatory frameworks

Abstract

This study investigates the relationship between algorithmic trading and market efficiency in European equity markets from 2024 to 2026. Utilizing a robust empirical framework, we collected high-frequency trading data from major European exchanges, applying advanced econometric models to analyze the impact of algorithmic strategies on volatility and price discovery. Our findings reveal that algorithmic trading significantly enhances market efficiency, evidenced by a reduction in bid-ask spreads and improved price dynamics. Additionally, we identify critical thresholds where algorithmic trading begins to exert diminishing returns on efficiency. These insights contribute to the ongoing discourse on the implications of algorithmic methodologies in financial markets, offering significant implications for policymakers and market participants regarding regulatory frameworks. The results emphasize the need for adaptive strategies to manage potential systemic risks associated with heightened algorithmic activity in trading environments.

Author Biographies

Morgan Harris, PhD

PhD
University of Mannheim
L7, 3-5, 68161 Mannheim, Germany

Taylor White, D.Sc

D.Sc
Frankfurt School of Finance & Management
Schaumainkai 602, 60596 Frankfurt, Germany

Jordan Jones, Associate Professor

Associate Professor
Humboldt University of Berlin
Unter den Linden 6, 10099 Berlin, Germany

Adrian Young, Professor

Professor
Technical University of Munich
Arcisstraße 21, 80333 Munich, Germany

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