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The preregistered SPY turn-of-month test found a positive return difference, but it did not meet the decision rule set before analysis. Its confidence interval included zero, its calendar-rotation test returned p = 0.212, and its net results fell short of the required matched-exposure benchmark. That is an inconclusive result for this test—not proof that the effect never existed or has disappeared.

What the test set out to measure

The turn-of-month effect is the historical claim that stock returns cluster around the transition between calendar months. This test used SPY daily returns from January 29, 1993, through September 24, 2026, covering 8,470 reported return bars. Its window was the final trading day of each month plus the first three trading days of the next month: four consecutive trading days, or 19.1% of the reported sample.

The author says the hypothesis and decision rules were frozen before examining the results. The preregistration file was identified by SHA-256 hash 04396d92f6a45d2ffebff0faff7c4e31cfde983e11d3bdfd5b5bc9f8e7d8cba2. The methods, figures, and hash are reported by the author; the underlying file and calculations have not been independently reproduced here. Read the author’s account of the SPY test.

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Why a positive estimate still failed

The average return in the selected window was higher than on other days, but the preregistered rule required all five of its decision clauses to pass. They did not. A positive point estimate is not, by itself, evidence that the effect is distinguishable from noise or clears a prespecified practical benchmark.

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Reported result What it means for the decision
In-window mean: +7.28 basis points per day; other-day mean: +3.33 bps/day; difference: +3.95 bps/day The estimated difference was positive.
Stationary-bootstrap 95% confidence interval for the difference: −2.02 to +9.80 bps/day The interval included zero, so the estimate did not establish a positive difference under this analysis.
Circular-rotation null test: p = 0.212 The result did not meet the test’s significance cutoff.
At 2 bps round-trip cost: annualized return 3.25%; matched-exposure null’s 95th percentile 3.38%; observed rule at the 93.8th percentile It fell short of the preregistered 95th-percentile hurdle.
At 5 bps round-trip cost: annualized return 2.88%; matched-exposure null’s 95th percentile 3.02%; observed rule at the 93.7th percentile It also fell short at this cost assumption.

The cost comparisons use the author’s reported annualized figures and tested cost assumptions; they are not independently reproduced. The benchmark matters: the author compared the four-bar rule with randomly positioned four-contiguous-bar windows within each month, matching exposure rather than comparing a partly invested strategy only with cash.

How the preregistered checks were designed

Calendar alignment and uncertainty

The author reports using a circular-rotation null to test whether returns aligned unusually well with the calendar labels while preserving the return series and the number and spacing of labeled days. The confidence interval came from a stationary bootstrap with a mean block length of 20 trading days and 10,000 resamples. These choices were specified in the preregistration, according to the author.

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Costs and execution timing

Costs were treated as part of the decision, not as an afterthought. The reported net-return comparisons at 2 and 5 bps round-trip costs both missed the required percentile threshold. Separately, the author reports that delaying execution by one bar reduced gross return from 3.50% to 3.01%. This illustrates why an apparent calendar pattern can depend on implementation assumptions as well as on the return estimate.

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Split-sample consistency

The estimated difference had the same sign in both reported halves: +5.84 bps/day in 1993–2009 and +2.03 bps/day in 2010–2026. Neither half was individually significant, and the late-half interval included zero, so the split-sample clause failed. Matching signs are not enough to establish a persistent effect when the intervals remain uncertain.

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What the stronger alternative window does—and does not—show

The author reports one alternative window with a larger estimated difference, +8.35 bps/day, and p = 0.020. It did not meet the stated Holm-corrected threshold of 0.0167 for three alternatives. The author also notes that the plausible search space was larger than those three windows, so correcting only across the registered subset would understate the selection problem. Treat this result as a follow-up hypothesis, not as a successful discovery.

How this compares with historical claims

Robert A. Ariel’s 1987 paper, “A monthly effect in stock returns,” reported that mean returns in its sample were positive immediately before and during the first half of calendar months, and indistinguishable from zero in the second half. That historical result used a different operational definition from this SPY test; it is context for the broader claim, not a direct replication. Ariel’s paper in the Journal of Financial Economics.

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A J.P. Morgan analysis reproduced in an SEC-hosted filing reported historical annualized S&P 500 returns through October 2022 of 26.06% for the first four trading days of the month, 1.53% for middle days, and 18.72% for the last two trading days. The corresponding share of positive-return days was 56%, 52%, and 54%. Those are historical group statistics, not a forecast or proof that a strategy could capture an edge after costs. The filing discusses possible explanations such as portfolio adjustments, pension reinvestment, and systematic retail mutual-fund investments, while cautioning that other factors may be responsible and future persistence is not assured. SEC-hosted filing containing the historical statistics and discussion. For broader academic context, see the AEA overview by Richard H. Thaler.

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What this failure can establish

  • For the specified SPY window, sample, statistics, and decision rule, the reported result did not clear the preregistered bar.
  • The point estimate was positive, but its uncertainty interval and rotation test did not establish a reliable calendar-linked difference under the reported methods.
  • The tested cost cases were near the matched-exposure threshold but remained below it.
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What it cannot establish

  • It does not prove that the turn-of-month effect never existed or is now absent. The author says the sample was too short to distinguish an effect of the estimated size from zero, estimating roughly 2.2 times the sample—about 75 years—would be needed at the stated volatility. That power estimate is the author’s, not an independent evaluation.
  • It does not establish whether the pattern appears in other indices, countries, or asset classes. The analysis covers SPY only and includes no cross-instrument replication.
  • It does not show that a historical calendar pattern will persist or can be traded profitably. Adjusted historical data can also be revised retroactively; the author says the conclusion is pinned to a hashed data snapshot.

Transparency details and limits

The author discloses that the first run calculated from an in-memory data pull while saving a snapshot rounded to six decimal places. The author says the I/O behavior was fixed and the same snapshot rerun, with the verdict and all five clause outcomes unchanged. This is an author disclosure, not an external audit of the data or code.

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The practical value here is the audit trail: a stated hypothesis, a fixed decision bar, an exposure-matched null, explicit costs, and a near-miss recorded as a miss. That makes the conclusion interpretable without turning a positive estimate into a claim of discovery.

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