Definition. A factor-zoo paper is a meta-study: instead of proposing an anomaly, it audits hundreds of published ones under a single methodology — common data, common construction, and multiple-testing corrections sized to the field’s collective search. The genre’s landmarks — Harvey-Liu-Zhu’s t ≈ 3 hurdle, Hou-Xue-Zhang’s mass replication, McLean-Pontiff’s post-publication decay estimates — are among the most consequential papers in asset pricing, and among the most misread.
Why honest studies disagree so much
Replication rates across factor-zoo studies range from grim (~35–50%) to reassuring (~80%+) on overlapping factor sets. The disagreement is methodological, and decoding it is the whole skill:
- Construction choices: equal- vs value-weighting, micro-cap inclusion, breakpoints — the harsh studies use constructions that mute micro-cap noise; the generous ones follow original papers’ choices. Both are defensible; they answer different questions (“is there a tradeable premium?” vs “did the authors report their sample accurately?”).
- The correction’s severity: a Bonferroni-style correction across 400 factors is brutal; false-discovery-rate control is milder; hierarchical/Bayesian approaches milder still. The reported replication rate is largely a function of this dial.
- The universe of factors counted: auditing every published anomaly vs the subset with strong priors changes denominators wholesale.
- Sample updates: extending samples past publication mixes replication failure with genuine decay — different diagnoses, same symptom.
Reading rule: extract the study’s answer to “replicates under whose construction, corrected how?” before quoting its headline rate. A factor “failing” in one study and “passing” in another is usually both — under different, stated rules.
What survives everyone’s methodology
The convergent findings across the genre, stated conservatively: a small core of factors (market, profitability/quality-family, momentum-family, value-family in some constructions) clears even harsh corrections; post-publication returns decay substantially — McLean-Pontiff-style estimates put the decline around a third to a half, blending arbitrage and reversal of data-mining luck; micro-caps carry the zoo — a large share of anomalies live where costs and survivorship issues concentrate and capacity doesn’t; and correlated factors are few underlying bets — hundreds of published factors compress into a handful of statistical clusters, so “N factors replicate” overstates independent evidence exactly the way correlated trades overstate sample size.
The practitioner translation
- A zoo paper is the best prior generator available: before evaluating any new factor paper, know which cluster it belongs to and how that cluster fared under harsh construction.
- Treat “fails harsh replication” as capacity information, not falsity: an equal-weighted-only anomaly is a statement about where the effect lives.
- The decay estimates are your default haircut: whatever premium you estimate from a published factor’s history, the zoo literature says to expect a large fraction less going forward.
The checklist for reading one
- ☐ Construction rules and weighting stated and understood
- ☐ Correction method identified; severity dial noted
- ☐ Replication rate re-read as conditional on both
- ☐ Factor-cluster structure reported (or reconstruct it)
- ☐ In-sample vs post-publication failure distinguished
- ☐ The study’s own trial count considered — meta-studies make choices too
Questions to ask
- Under this study’s rules, would the market factor itself replicate? (A useful severity calibration.)
- Which specific factors flip status relative to other zoo studies, and does the construction difference explain it?
- Does the study distinguish “never real” from “real, then arbitraged”?
- What does it imply for the factor cluster behind the paper you actually care about?
How our scoring relates
Zoo papers themselves tend to score at the top of our rigor axis — they are, in effect, the field auditing itself with disclosed methodology, the same ethos as our replication reviews. For the individual factor papers they audit, our scores and the zoo’s verdicts are complementary: we score the paper’s internal evidence quality; the zoo scores its survival under external, uniform rules. A paper strong on both is the rare real thing.
The framework upstream: multiple testing & significance → · evaluating factor papers → · The mechanism: what makes a factor tradable →