Artificial Intelligence, Algorithmic Trading, and Financial Market Microstructure: A Systematic Literature Review

Abstract

The past two decades have witnessed a structural transformation in financial market microstructure: human traders have been displaced by algorithmic agents operating at microsecond timescales. This paper provides a systematic review of the academic literature on how algorithmic trading (AT), high-frequency trading (HFT), and artificial intelligence—including large language models (LLMs) and machine learning classifiers—have reshaped three foundational dimensions of market quality: liquidity provision, price discovery, and market stability. Following a PRISMAcompliant protocol, we screened 847 initial records across Web of Science, Scopus, SSRN, and Google Scholar, ultimately synthesizing 60 studies meeting pre-specified inclusion criteria. We organize the literature around three central debates. The first concerns whether algorithmic market-making constitutes genuine liquidity supply or phantom liquidity that evaporates under stress, amplifying flash crashes across equity, Treasury, and foreign exchange markets. The second examines the tension between AIenhanced informational efficiency and systemic fragility induced by algorithmic herding, correlated machine-learning strategies, and retail-driven social media trading. The third surveys the regulatory challenge of detecting AI-facilitated market manipulation—including spoofing and quote stuffing—through natural language processing and high-frequency surveillance. Drawing on foundational contributions from Kyle (1985), Glosten and Milgrom (1985), and Budish, Cramton, and Shim (2015), we characterize the current paradigm as "algorithmic ecology"—a coevolutionary ecosystem in which AI agents, human traders, exchanges, and regulators continuously adapt to one another's strategies. We identify a fundamental paradigm shift: market microstructure research must transition from models of rational human AI, Algorithmic Trading & Market Microstructure agents toward frameworks accommodating the strategic behavior, correlated failures, and emergent dynamics of algorithmic agents.

Author abstract from the research manuscript. Typography and line breaks have been normalized; the linked source may show a different manuscript version.