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A note on AI. QuanterLab is a quantitative finance research platform, and every number in this study comes from a run on the platform. The hypothesis, the parameter choices, the validation design and the conclusions belong to the author. Runs execute on point-in-time data with walk-forward validation, and each study ships with its methodology and logs, so a reader can reconstruct the result instead of trusting it. I use AI to edit and structure the prose; it does not generate results, produce numbers, or decide what a study concludes.

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Research note · commentary across registered studies

Arguments About Personality - a research note on the Fama-French series

What this is. A research note: the author's commentary across 4 registered studies. It registers no hypothesis and runs no walk of its own. Every figure and every number in the table below is generated live from the cited studies' frozen artifacts, the same records their own pages render.

Four of the most celebrated arguments in quantitative finance, run through one honest machine: the same universe, the same yardstick, twenty sealed years each, and - three times out of four - every dividend, every trading cost, and a benchmark allowed to keep its own dividends. Not one verdict was rankable. That is not the disappointment it sounds like.

Over one week we ran four famous cross-sectional signals through the same machine. Each became two portfolios of thirty S&P 500 stocks, selected point-in-time, equal weighted, re-selected annually, walked across twenty sealed one-year windows from 2006 through 2025 - each window registered before it ran, each result frozen when it landed. One book appeared in every contest: thirty stocks ranked on earnings yield. Plain cheapness, the oldest screen in the book. It was the yardstick.

The first study put the yardstick itself on trial. Academic value was built on book-to-market: Fama and French wrote in 1992 that it absorbs the earnings yield, and forty years of factor models took them at their word. Twenty sealed years of large caps found the two measures selecting almost disjoint portfolios - by 2023 they shared exactly one stock in thirty - diverging by ten to twenty points in single years, and finishing a statistical dead heat. The absorption claim, where most invested money actually sits, is moot.

The choice that washes out - book-to-market against the earnings yield it displaced, twenty sealed windows of the S&P 500

Out-of-sample equity: normalised growth (1.00x = break even)-0.09x2.71x5.50x2006200920122015201820212024
 Book-to-market (8.5%/yr, Sharpe 0.456) ·  Earnings yield (8.0%/yr, Sharpe 0.444) · benchmark grey · windows 9-11 · bootstrap 57.1% · price returns. Rendered live from the study's frozen artifact.

Then came the challengers, each measured in total returns with real costs.

Dividend yield - the income aisle's favorite - collected 4.6 percent a year in cash, more than double the index's rate, and compounded almost exactly at the honest index's pace, on a worse Sharpe than the index. The cash was real and spendable; the price leg gave it back. The gap to the cheapness book is not statistically certifiable - the two books diverge by about twelve points in a typical year, and at that noise roughly two hundred and eighty years would be needed to certify the observed lean - but the structure is not noise: the market handed the income out of one pocket and took it from the other.

Paid to wait, paid less - dividend yield against earnings yield in total returns, twenty sealed windows of the S&P 500

Out-of-sample equity: normalised growth (1.00x = break even)-0.18x4.13x8.45x2006200920122015201820212024
 Dividend yield (9.6%/yr, Sharpe 0.505) ·  Earnings yield (11.0%/yr, Sharpe 0.565) · benchmark grey · windows 9-11 · bootstrap 29.1% · total returns, net of costs. Rendered live from the study's frozen artifact.

Gross profitability - Novy-Marx's 'other side of value', Fama-French's RMW - produced the series' best risk-adjusted record: the highest Sharpe of the quartet on volatility below the index's own, the only book of the three total-return studies to beat the honest benchmark on return and risk at once (a description of one path, not a certified premium). Against the cheapness yardstick: ten windows each, a coin flip. The two books shared a median of ONE name in thirty - the most disjoint pair in the series - and the profitability book won every crisis on its record, COVID by thirty-eight points.

The compounders and the cigar butts - gross profitability against earnings yield in total returns, twenty sealed windows of the S&P 500

Out-of-sample equity: normalised growth (1.00x = break even)-0.20x4.85x9.90x2006200920122015201820212024
 Gross profitability (11.8%/yr, Sharpe 0.650) ·  Earnings yield (11.0%/yr, Sharpe 0.565) · benchmark grey · windows 10-10 · bootstrap 48.5% · total returns, net of costs. Rendered live from the study's frozen artifact.

Net share issuance - the buyback signal Fama-French ranked among the strongest anomalies - finished the tightest race of all: two tenths of a point of annual return apart after twenty years. Its holder was paid an index-like two percent in dividends and received the rest through a quietly shrinking float - the mechanical opposite of the dividend book. Its one famous vice is printed in its ledger: in the 2007 window, corporate America repurchasing record volumes at peak prices into the coming crash, it trailed cheapness by fourteen points.

Paid in fewer shares - net issuance against earnings yield in total returns, twenty sealed windows of the S&P 500

Out-of-sample equity: normalised growth (1.00x = break even)-0.09x4.18x8.44x2006200920122015201820212024
 Low net issuance (10.8%/yr, Sharpe 0.583) ·  Earnings yield (11.0%/yr, Sharpe 0.565) · benchmark grey · windows 9-11 · bootstrap 35.2% · total returns, net of costs. Rendered live from the study's frozen artifact.

Four contests. Four statistical ties. Every lean against the yardstick - 0.2 to 1.4 points a year - inside the noise. If the series had been run to crown a winner, it failed four times. It was not run to crown a winner, and the ties are not the finding.

The finding is what stayed measurable after the ranks dissolved. These portfolios are not variations on a theme - they are nearly strangers. The profitability and cheapness books agreed on one stock in thirty; the dividend and cheapness books on four; in several windows, on none at all. They pay in different currency: 4.6 percent cash from the dividend book, 1.3 from the profitability book, a shrinking share count from the buyback book. They shelter in different storms: profitability won every crisis on its record, while the dividend book cushioned the rate shocks and failed exactly once - in 2008, when high yield meant bank yield about to be cut. Yield is a promise written by the past; earnings are the capacity to keep it.

One pattern crossed the studies uninvited: since 2022, plain cheapness has won every single window against both quality and buybacks - nine consecutive contests across two studies. (Honesty requires the footnote: two of that era's windows had been run as unregistered engine rehearsals before one of those studies sealed; no design changed, and the sweep's remaining windows ran sealed-first. It is an observation found in the record, not a claim made before it.) Whatever regime arrived with higher rates has, so far, paid the cigar butts.

So the quartet's answer to 'which factor is best?' is that the question is malformed at this altitude. Among the five hundred most-watched stocks in the world, measured in the units a holder actually receives, the famous signals are indistinguishable in destination and utterly distinguishable in character. The choice between them is not a forecast of return. It is a choice of experience: what you hold, how you are paid, when you suffer, and whether your risk can be drawn in advance. Twenty sealed years cannot rank the returns. They can describe the personalities with precision.

The cross-section's famous arguments, measured honestly, are arguments about personality.

The scoreboard. Each row is generated from that study's frozen artifact. Bases differ by study and are stated under each figure; bases are never mixed inside a row.
StudyArmsCAGRSharpe BootstrapWindows
The choice that washes out -... Book-to-market vs Earnings yield 8.5 / 8.0 0.456 / 0.444 57.1% 9-11
Paid to wait, paid less - dividend yield... Dividend yield vs Earnings yield 9.6 / 11.0 0.505 / 0.565 29.1% 9-11
The compounders and the cigar butts -... Gross profitability vs Earnings yield 11.8 / 11.0 0.650 / 0.565 48.5% 10-10
Paid in fewer shares - net issuance... Low net issuance vs Earnings yield 10.8 / 11.0 0.583 / 0.565 35.2% 9-11

How to read this note

Every study cited here is a two-arm comparative walk on the point-in-time S&P 500: thirty names per arm, equal weight, long only, annual re-selection, twenty one-year windows anchored each January from 2006, each window's hypothesis registered before running and its report frozen at execution. The dividend, profitability and issuance studies are measured in total returns (ex-date dividend credits, split-adjusted amounts) net of ten basis points per one-way traded dollar, against an equal-weight total-return benchmark; the book-to-market study is price returns against a price benchmark, labeled as such, and its row in the table above must be read on that basis.

'Bootstrap' in the figures and table is the fraction of 2,000 block-resampled twenty-year paths on which the first arm finishes ahead - a path-level statistic, not a daily win rate. Every 'cannot certify' in this note is that bootstrap speaking. This note registers nothing of its own; the four full records - every window, every holding, every projection cone - are open at the links above.

QuanterLab · Research note b503073fd49f · 2026-08-16. This page is COMMENTARY, hand-written by the author across the registered studies it cites; it registers no hypothesis of its own. Its figures and table are generated live from those studies' frozen artifacts. Educational research, not investment advice: every result shown is simulated, and nothing here is a recommendation to buy or sell any security.
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A note on AI. QuanterLab is a quantitative finance research platform, and every number in this study comes from a run on the platform. The hypothesis, the parameter choices, the validation design and the conclusions belong to the author. Runs execute on point-in-time data with walk-forward validation, and each study ships with its methodology and logs, so a reader can reconstruct the result instead of trusting it. I use AI to edit and structure the prose; it does not generate results, produce numbers, or decide what a study concludes.