# Jf Empirical Design > Use when designing or stress-testing an asset-pricing test for a The Journal of Finance (JF) manuscript — factor models, Fama–MacBeth vs. panel, standard-error corrections, out-of-sample discipline. For corporate causal claims use jf-identification. - Skill: `thedixitjain/jf-empirical-design` (Agent Skill) - Install (CLI): `npx skillmds add thedixitjain/jf-empirical-design` - Raw SKILL.md: https://api.skillmd.com/api/skills/thedixitjain/jf-empirical-design/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Coding & Dev Tools - Author: thedixitjain (https://skillmd.com/u/thedixitjain) - Updated: 2026-09-08 - Page: https://skillmd.com/skills/thedixitjain/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. ## Execution bridge (StatsPAI / Stata MCP) Run the asset-pricing battery, don't just specify it. Full map: [`shared-resources/empirical-methods/execution-with-mcp.md`](../../../shared-resources/empirical-methods/execution-with-mcp.md). JF asset-pricing instantiation: - **Factor regressions / time-series alphas:** `feols` with the right SEs (`vcov="HC3"` or Newey–West / clustered) — read the alpha and t off the return, not off a memory of the spec. - **The factor-zoo haircut (the JF-salient one):** after disclosing how many signals were screened, apply `romano_wolf` (step-down, accounts for cross-signal correlation) or `benjamini_hochberg`, and report the alpha that survives the adjusted threshold — the executed Harvey–Liu–Zhu discipline. - **Fama–MacBeth + Shanken EIV correction** are Stata-canonical: run them through the Stata MCP (`mcp__stata-mcp__stata_do`) with the vendored `resources/code/` skeleton (`asreg`/`xtfmb`, Shanken-adjusted SEs) and reconcile to the Python alpha. - **Emit JF-format exhibits** with `etable`; hand formatting to `jf-tables-figures`. Report the **economic magnitude** (bps/month alpha, Sharpe gain) the body requires; the full factor-model grid and all screened signals go to the bundled Internet Appendix. If a server is not connected, adapt `resources/code/` and flag any unverified number. ## 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 ``` --- **Source:** [`brycewang-stanford/Awesome-Journal-Skills`](https://github.com/brycewang-stanford/Awesome-Journal-Skills) → `Journal-of-Finance-Skills/skills/jf-empirical-design/SKILL.md`