Artificial Intelligence and Financial Volatility: From Model Augmentation to Market Disruption — A Systematic Critical Review

Abstract

The rapid integration of artificial intelligence across financial markets has generated a heterogeneous literature whose findings are frequently compressed into conflicting partial verdicts about whether AI stabilizes or destabilizes markets. This review organizes that literature around a Technology–Shock–Agent (TSA) framework, distinguishing the epistemic channel through which AI improves forecasting from the channels through which AI events and AI trading agents affect realized market outcomes. Following a PRISMA-guided systematic review, we identify 72 Core studies (2015–2026) drawn from 140 unique sources. The Technology arm documents forecasting-accuracy improvements but offers only epistemic, not market-outcome, evidence. The Shock arm supplies direct market-outcome evidence, including seven causal or quasi-experimental entries. Twenty-one direct market-outcome sources are directionally consistent with volatility compression in normal conditions and amplification under stress — consistent with, but not establishing, a causal regularity. Critically, zero qualifying studies isolate adaptive AI trading agents during real stress events using causal identification. We conclude that precautionary regulatory action is warranted while full empirical confirmation awaits future research.

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