TL;DR
The cross-sectional literature has produced hundreds of published "factors," but a large share fail to replicate, decay sharply after publication, or vanish under honest multiple-testing corrections. This meta-literature — on p-hacking, out-of-sample validation, and what actually survives — is the intellectual core ConvexPi is built around.
A 30-year arc
Fama & French (1993, 2015) — the 3- and 5-factor models that organized the field.
Harvey, Liu & Zhu (2016) — …and the Cross-Section of Expected Returns: catalog ~316 factors and argue the right t-stat hurdle, after multiple testing, is closer to 3.0 than 2.0.
McLean & Pontiff (2016) — published anomalies decay ~30–58% post-publication, evidence that much "alpha" is statistical and/or arbitraged away.
Hou, Xue & Zhang (2015, 2020) — Digesting Anomalies / the replication study: many anomalies are insignificant once microcaps are handled properly; propose the q-factor model.
Chen & Zimmermann (2022) — Open Source Cross-Sectional Asset Pricing: a transparent, reproducible anomaly dataset (the OSAP project our replications validate against).
Bailey & López de Prado (2014) — the Deflated Sharpe Ratio: adjust performance for the number of trials.
Feng, Giglio & Xiu (2020); Kozak, Nagel & Santosh (2020); Kelly, Pruitt & Su (2019); Gu, Kelly & Xiu (2020) — taming the zoo with ML/shrinkage (Lasso factor selection, sparse SDFs, IPCA, deep learning).
Sub-threads
Multiple-testing corrections · post-publication decay · replication failures & microcap effects · the deflated Sharpe ratio · ML/shrinkage for factor selection · open, reproducible datasets.
Why it matters
A factor "discovered" by scanning thousands of candidates needs a far higher bar than one tested once — otherwise you publish noise. The corrections (higher t-hurdles, FDR, deflated Sharpe), the discipline (true out-of-sample / walk-forward), and the antidote (transparent, clean-room replication) are what separate durable signal from the zoo.
The dark side
Data mining at scale — cheap compute + many datasets manufacture spurious factors faster than they can be vetted.
Publication & survivorship bias — only the winning specification gets published; failures stay in the drawer.
Fragility — small changes in universe, weighting, or sample flip many "anomalies."
Does it survive out of sample?
This is the "does it survive" topic. The honest answer: a minority of the zoo survives rigorous OOS testing — momentum, value, profitability/quality, and the low-risk family are among the more durable; many accounting/microstructure anomalies have decayed. Our replications recompute the canon clean-room and score every one on the holdout (the McLean-Pontiff test), and the anomaly graveyard tracks what died.
Run it yourself
Curriculum — Mission 1 makes you feel overfitting; Mission 3 (alpha discovery) drills multiple-testing, FDR, and walk-forward validation; Mission 8 covers transaction-cost erosion.
Replications — the clean-room antidote: idea + DOI, recomputed and OOS-scored.
Competitions — graded on out-of-sample Sharpe, because in-sample curve-fitting won't save you.
