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Liquidity at the Speed of AI: Algorithmic Trading and Systemic Risk Amplification

Nag, Arindam (2026): Liquidity at the Speed of AI: Algorithmic Trading and Systemic Risk Amplification.

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Abstract

This paper investigates whether artificial intelligence amplifies systemic risk in equity markets using daily data spanning February 2023 to December 2025, comprising 721 observations across the CBOE Volatility Index, S&P 500 and NASDAQ Composite returns, abnormal trading volume, and the Amihud illiquidity ratio. Employing descriptive statistical analysis, an event study framework, OLS regression with Newey-West HAC-corrected standard errors, and a six-lag Vector Autoregression, the results provide evidence broadly consistent with systemic risk amplification through the liquidity withdrawal channel. The regression results indicate that market illiquidity, as measured by the Amihud ratio, is a statistically significant predictor of volatility (coefficient = 1,144,957; p < 0.01), while lagged volatility exhibits strong persistence (coefficient = 0.928; p < 0.01), with the model explaining 94.7 percent of daily VIX variation. Event study analysis identifies Five discrete stress episodes during which the VIX rises by an average of 27.3 percent, accompanied by a 64.7 percent increase in illiquidity and an 11.6 percent rise in abnormal trading volume. VAR analysis reveals a bidirectional relationship between volatility and trading activity, consistent with AI-driven feedback dynamics, though the direct effect of abnormal volume on volatility is not robust under HAC correction. The findings carry significant implications for financial stability monitoring and regulatory design in AI-transformed markets.

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