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

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  • Nag, Arindam

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

Suggested Citation

  • Nag, Arindam, 2026. "Liquidity at the Speed of AI: Algorithmic Trading and Systemic Risk Amplification," MPRA Paper 128853, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:128853
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    References listed on IDEAS

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    Keywords

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    JEL classification:

    • G0 - Financial Economics - - General
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G3 - Financial Economics - - Corporate Finance and Governance
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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