jf-empirical-design

Asset-Pricing Test Design (jf-empirical-design)

When to trigger

• You have a candidate predictor / anomaly / factor and must decide how to test it
• You are unsure whether to run Fama–MacBeth, time-series factor regressions, or a panel
• You report t-stats but have not addressed the standard-error subtleties of cross-sectional asset pricing
• A referee will ask "is this data mining / does it survive multiple testing / does it work out of sample?"

Scope: this skill is for asset-pricing tests. For corporate/empirical causal effects, route to jf-identification.

Choosing the test

Goal	Workhorse design
Does characteristic X price the cross-section?	Fama–MacBeth cross-sectional regressions + portfolio sorts
Is a candidate factor priced / spanned?	Time-series regressions; GRS test; spanning vs. established factors
Compare competing factor models	Alphas of test assets; max-Sharpe / HJ distance; model comparison
Does a signal predict returns?	Predictive regressions + long-short; in/out-of-sample R² (Campbell–Thompson)
Panel with firm/time variation	Panel with appropriate fixed effects and clustering

JF-specific standards

JF asset-pricing referees engage the JF-published canon — Sharpe (1964) CAPM, Fama–French (1992), Jegadeesh–Titman (1993) momentum, Carhart (1997) — and expect you to benchmark against the right factors (recall the FF three-factor model is JFE 1993). They also expect:

• Errors-in-variables / Shanken correction on Fama–MacBeth standard errors where betas are estimated.
• Multiple-testing discipline: a new anomaly must survive the "factor zoo" critique (Harvey, Liu & Zhu, JF) — adjusted t-thresholds, not the naive 1.96.
• Out-of-sample evidence for predictability claims, not just in-sample fit.
• Economic magnitude (Sharpe gain, alpha in bps), since JF writes for a general-interest reader.
• Exhaustive specifications go to the Internet Appendix (bundled in the same PDF; see jf-internet-appendix), keeping the body within 60 pages.

Worked vignette — a risk-vs-mispricing horse race

Illustrative numbers. A new characteristic predicts the cross-section: a long–short decile spread of 0.60%/month, raw t = 3.3. The JF question is not "is it significant?" but "is it risk or mispricing, and does it survive the canon?"

1. Benchmark against the right factors. Regress on Fama–French five factors + momentum; suppose the alpha is 0.42%/month (t = 2.9). The shrinkage from 0.60 already shows part of the spread is known-factor exposure.
2. Clear the multiple-testing bar. If mined from many candidates, t = 2.9 must beat the factor-zoo cutoff (Harvey–Liu–Zhu argue ~3.0, illustratively); a borderline t is a referee magnet.
3. Risk vs. mispricing. For risk, show the characteristic loads on priced covariance (GRS test). For mispricing, show the alpha concentrates among hard-to-arbitrage names and decays after ~6 months. JF rewards a paper that adjudicates.
4. Out-of-sample. Campbell–Thompson out-of-sample R² for the predictive version; in-sample-only reads as data-snooping.
5. Economic magnitude. Translate to an annualized Sharpe gain (≈ 0.35) so the general-interest reader sees the stake.

The full grid — all factor models, subperiods, cost nets — goes to the Internet Appendix; the body carries the alpha table, GRS test, and OOS result.

Referee-pushback patterns and the JF-specific fix

Pushback you will hear	JF-specific fix
"It's just exposure to known factors"	Report alphas vs. FF5 + momentum; show the residual spread
"t = 3.3 after mining is not 1.96 territory"	Apply the factor-zoo-adjusted threshold; disclose the search
"Is this risk or mispricing?"	Run the horse race (GRS / covariance vs. arbitrage-limits decay)
"In-sample only"	Add out-of-sample R² (Campbell–Thompson) or a holdout
"These are illiquid microcaps"	Value-weighted, NYSE-breakpoint, post-cost version

Calibration anchors for JF asset pricing

• The flagship rewards tests that adjudicate a first-order question (covariance risk vs. correctable mispricing) over a bare predictive regression.
• SE and multiple-testing conventions evolve; the EIV/Shanken correction and factor-zoo threshold are durable, but confirm the favored estimators and benchmark factor sets against recent JF issues.
• Economic magnitude in interpretable units (Sharpe gain, bps alpha) is non-negotiable in the body, since JF writes for a non-specialist reader.

Checklist

• Test matched to the question (FM / time-series / panel)
• Standard errors correct for the design (Shanken, NW, clustering)
• New factor/anomaly survives a multiple-testing-adjusted threshold
• Out-of-sample check for any predictability claim
• Benchmarked against the standard factor models, attributed correctly
• Economic magnitude reported, not just t-stats

Anti-patterns

• Reporting raw t > 1.96 as decisive after mining many signals (the factor-zoo trap)
• Fama–MacBeth t-stats with no EIV/Shanken adjustment
• In-sample-only predictability dressed up as a discovery
• Crowding every robustness table into the body instead of the Internet Appendix

Output format

【Test chosen + why】...
【SE correction (Shanken/NW/cluster)】...
【Multiple-testing threshold cleared?】yes / no
【Out-of-sample evidence?】yes / no
【Economic magnitude】...
【Next step】jf-robustness