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What Drives Crash Prediction? Machine Learning Ablation Evidence from Market Microstructure

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We investigate what makes mini flash crashes predictable using a feature ablation framework that decomposes the necessity and sufficiency of 253 market microstructure features. Across more than 3,600 out-of-sample experiments on SP 100 stocks, we find that crash prediction is driven primarily by liquidity and information asymmetry and relies on minutes, rather than milliseconds, of prior information. Conventional feature importance measures overstate the role of microstructure noise because its predictive value arises only through interactions with other features. Cross-sectionally, VPIN is the only significant determinant of stock-level crash predictability.

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  • Bo Liu & Ke Xu, 2026. "What Drives Crash Prediction? Machine Learning Ablation Evidence from Market Microstructure," Department Discussion Papers 2604, Department of Economics, University of Victoria.
    • Handle: RePEc:vic:vicddp:2604
    Note: ISSN 1914-2838 JEL Classification:G12, G14, G17, C53, C55
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