The Temporal Structural Forecasting research using stock market data is extensive and far-reaching. All of the research hypotheses and forecast methodologies have been preregistered on Zenodo in advance of analyzing any forecast data or results. These omnibus preregistrations are specifically designed to allow for multiple studies testing a full range of hypotheses and configurations on the same out-of-sample universe of 346 S&P500 stocks with sufficient historical data to generate 20 full years of forecasts.
The most relevant preregistrations are presented here with the Zenodo DOI links.
Temporal Structural Forecasting: A Comprehensive Empirical Refutation of Weak-Form Market Efficiency
This registered report presents the most comprehensive empirical challenge to weak-form market efficiency ever assembled. We introduce Temporal Structural Forecasting (TSF), a methodology that identifies exploitable patterns in historical price data through multi-cycle seasonal decomposition. The study tests 44 preregistered hypotheses across four papers using 346 S&P 500 stocks spanning 11 GICS sectors over 20 years (2006–2025). We establish four independent refutation paths: (1) predictable structure exists in price data, (2) entry timing is exploitable after transaction costs, (3) exit timing is independently exploitable regardless of entry methodology, and (4) temporal structure improves factor portfolio returns. Preliminary results from a 30-stock pilot study (2015–2024) confirm all four refutation paths with 27/44 hypotheses (61%) supported. Most notably, applying TSF exit signals to benchmark strategies improved excess returns in 72.7% of cases (p = 1.19 × 10⁻⁵), demonstrating that exit timing constitutes an independent market inefficiency—a finding that uses zero TSF entry signals. The methodology, hypotheses, and analysis plan are locked via Zenodo deposit prior to primary data analysis on the 346-stock universe.
https://doi.org/10.5281/zenodo.18188491
Factor Betas Are Path-Dependent Regression Artifacts: Falsifying Fama-French with 6,560 Controlled Experiments
Fama and French (1996) called momentum “the main embarrassment” of their three-factor model—the one timing anomaly their framework could not explain away. We demonstrate that this embarrassment was not an exception but a warning: the entire factor-based methodology for dismissing timing anomalies is invalid. Using 6,560 controlled experiments where paired portfolios hold identical stocks with identical weights entered on identical dates—differing only in exit timing—we show that factor loadings fail equivalence tests at an 86% rate. The failures are not marginal: loading differences exceed 0.45 in magnitude. Random entry strategies with zero predictive content produce massive loading shifts. A 10-stock single-sector control eliminates diversification explanations and still shows 96% failure. These results prove that factor loadings measure return-path covariances, not holdings-based risk exposure. The standard interpretation—that loadings reveal what risks a portfolio bears—is empirically false when timing varies. Every paper that used Fama-French regressions to conclude “alpha explained by factor exposure” for a timing strategy was applying a tool incapable of making that determination. Momentum survived as an acknowledged anomaly only because the broken dismissal tool happened to fail visibly for that case. The Halloween effect, January effect, and hundreds of other timing signals were dismissed by the same invalid procedure. Thirty years of factor-based anomaly evaluation must be reconsidered (Preregistered: https://doi.org/10.5281/zenodo.18304121).
https://doi.org/10.5281/zenodo.18330795
Regime-Conditional Factor Rotation: Testing TSF Timing Signals for Defensive Factor Alpha Generation
Defensive factor strategies (low volatility, low beta, low momentum) systematically underperform during bull markets, creating a structural problem for factor fund managers who lose clients precisely when risk management is most valuable. This study tests whether Temporal Structural Forecasting (TSF) timing signals can solve this problem by generating alpha for defensive factors during bull market conditions, and whether regime-conditional factor rotation (FLIP strategies) can outperform static single-factor approaches. The preregistered analysis covers 346 S&P 500 stocks across 20 years (2006-2025), testing 18 hypotheses across 11 universes, 6 diversification levels, 4 time periods, and 18 TSF configurations. This document specifies the complete methodology, hypotheses, and confirmation criteria prior to data analysis.
https://doi.org/10.5281/zenodo.18190988