The Case Against Factor Timing

Systematic equity managers commonly apply static factor weights when ranking stocks by multi-factor scores. This paper asks whether that assumption holds in the data. Using Fama-French long-short portfolio returns from July 1963 to December 2025 (750 months), the paper tests whether factor premiums are genuinely time-varying or whether a constant weighting scheme is statistically justified. Two competing estimates of each factor’s premium are compared: an expanding-window average (representing the static approach) and a 60-month rolling average (representing a time-varying approach). Structural break tests applied to monthly returns fail to detect any genuine shifts in the average premium for any of the six factors considered, even though the same test wrongly suggests strong shifts when applied to the rolling-average series instead of the raw returns. A separate forecasting test across 690 out-of-sample months confirms this: the rolling estimator does not improve predictive accuracy for the equal-weighted factor composite at any window length tested, and performs significantly worse than the static estimator for the profitability and investment factors, with no factor showing a significant improvement. The practical implication is that strategies which adjust factor weights in response to rolling-premium estimates are responding to statistical noise rather than genuine regime shifts, and incur measurable cost in doing so.