Spurious Predictability in Financial Machine Learning

Battle Count: 57

This paper is highly relevant to quantitative trading as it directly addresses the core problem of distinguishing genuine predictive signals from methodological artifacts in backtesting. The falsification audit framework provides a practical pre-deployment gate for trading strategies, the inflation diagnostics quantify the gap between optimized in-sample and realizable out-of-sample performance, and the empirical case studies demonstrate common failure modes (deep learning on SPY, cross-sectional ML with beta confounds, volatility forecasting validation inflation). The framework is applicable to any quantitative strategy development pipeline and provides actionable guidance for practitioners on enforcing causal validation, pricing adaptive search as model risk, and maintaining defensible research records.

Implementation requires: (1) constructing five synthetic null environments with specific DGP specifications; (2) running Monte Carlo simulations (N=1,000 replications) across environments; (3) implementing strict walk-forward validation with HAC standard errors; (4) computing effective multiplicity via spectral participation ratio with shrinkage estimation; (5) calibrating environment-specific critical values with Bonferroni corrections; (6) implementing the Blind-Parameter Protocol with public/audit parameter sets. The QuantAudit R package is intended to simplify this, but the full pipeline requires careful attention to temporal ordering, disjointness constraints, and proper handling of finite-sample HAC distortions. The theoretical framework (extreme-value scaling, Gaussian maximal inequalities) adds conceptual complexity.

The paper introduces the QuantAudit R package as a reference implementation with modules for synthetic null generation, strict walk-forward validation, and inflation diagnostics. Full replication scripts are prepared for public release via GitHub and CRAN upon journal publication. However, as of the arXiv preprint, the code is not yet publicly available. The paper provides detailed mathematical specifications for all five null environments, default calibrations, and simulation parameters (N=1,000 Monte Carlo replications). Empirical applications use standard datasets (SPY, Fama-French 25 Portfolios).