False Discovery Control for Factors
A statistical method for asset-pricing research that adjusts significance thresholds so that testing hundreds of candidate factors doesn't manufacture "significant" ones by chance alone.
If a researcher tests 200 candidate factors at the usual 5% significance level, roughly 10 of them will look statistically significant purely by chance, even if none of them actually predict returns. This is the multiple-testing problem applied to asset pricing, and Giglio, Liao and Xiu built tools to handle it properly at the scale the factor zoo actually operates at — thousands of candidate signals, many of them correlated with each other rather than independent tests.
Their approach adapts false-discovery-rate control (originally from genomics, where the same problem of testing thousands of genes at once arises) to asset pricing's specific quirks: returns are noisy, factors are correlated, and the number of candidates keeps growing as researchers publish more. The output is an adjusted significance bar — much stricter than the ordinary 5% cutoff — that keeps the expected share of false "discoveries" among reported significant factors under control.
Testing many candidate factors inflates the number of accidental "significant" results; false-discovery-rate methods raise the bar for significance in proportion to how many factors were tried, so a reported "alpha" survives only if it clears a much higher threshold than a single, pre-registered test would need.
Worked example. A researcher runs significance tests on 500 candidate signals at the standard 5% level and finds 30 "significant" ones. Applying false-discovery-rate control at a 5% target, the adjusted threshold shrinks the surviving list to just 6 signals — the other 24 are judged likely artifacts of testing so many candidates at once, not genuine return predictors.
Practice in interviews
Further reading
- Giglio, Liao & Xiu, 'Thousands of Alpha Tests'