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Spurious Predictability in Financial Machine Learning

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  • Sotirios D. Nikolopoulos

Abstract

Adaptive specification search generates statistically significant backtests even under martingale-difference nulls. We introduce a falsification audit testing complete predictive workflows against synthetic reference classes, including zero-predictability environments and microstructure placebos. Workflows generating significant walk-forward evidence in these environments are falsified. For passing workflows, we quantify selection-induced performance inflation using an absolute magnitude gap linking optimized in-sample evidence to disjoint walk-forward realizations, adjusted for effective multiplicity. Simulations validate extreme-value scaling under correlated searches and demonstrate detection power under genuine structure. Empirical case studies confirm that many apparent findings represent methodological artifacts rather than genuine predictability.

Suggested Citation

  • Sotirios D. Nikolopoulos, 2026. "Spurious Predictability in Financial Machine Learning," Papers 2604.15531, arXiv.org.
    • Handle: RePEc:arx:papers:2604.15531
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    References listed on IDEAS

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