Factor premia are easy to discover and hard to keep. The references below cover standard benchmarks, model selection in a crowded factor zoo, multiple-testing bias, and open replication — the core reading for anyone running cross-sectional strategies today.

Benchmarks and data

Start with the canonical factor series before testing new signals. Most anomaly papers use these as controls or portfolio sorts.

Kenneth French Data Library

Fama, French et al. · Ongoing · Research data

Three-, four-, and five-factor returns plus industry portfolios and international extensions. The standard benchmark for US equity factor research.

Data

Factor zoo and selection

Hundreds of published factors create a selection problem. Feng, Giglio, and Xiu provide a formal test for whether a new factor adds information beyond what is already known.

Replicating Anomalies

Hou, Xue, Zhang · 2020 · Review of Financial Studies

Replicates 452 published anomalies with NYSE breakpoints and value-weighted returns. 65% fail a |t| ≥ 1.96 hurdle; 82% fail Harvey-style multiple-testing thresholds. Essential context before treating any new factor as durable alpha. Author PDF

RFS

Open replication

Open Asset Pricing Signal Data

Chen, Zimmermann · Ongoing · Data release

Downloadable signal panels and replication scripts. Compare your factor construction against a public baseline.

Data

Practical checklist

  • Regress new signals on standard Fama-French factors before claiming alpha
  • Apply Harvey-Liu style significance thresholds, not raw t-stats
  • Check whether the signal replicates in open-source data releases
  • Monitor post-publication decay and capacity, not just in-sample spreads

See also our backtesting pitfalls guide for overfitting checks before live deployment.