Research

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.

This study tests whether temporal structure exists in equity price data and can be systematically exploited for position entry timing. Using a preregistered methodology, we generate specific limit order entry prices one week in advance for 346 S&P 500 constituents across all 11 GICS sectors. We test 1,386 parameter permutations over the 10-year period 2016–2025, encompassing 6 factor strategies, 7 forecast models, and 3 confidence thresholds.

This study subjects the TSF confidence interval framework to a rigorous structural integrity test across 14 major market disruptions spanning 17 years (2007–2024). The events range from sector-specific dislocations (the SVB bank failure) to global systemic crises (the Lehman Brothers bankruptcy and COVID-19 pandemic). For each event, we evaluate whether TSF’s 90% confidence intervals maintain their calibration—that is, whether realized prices remain within the predicted bounds at rates consistent with pre-shock baselines.

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.