--- name: rof-data-analysis description: Use when auditing Review of Finance empirical or theoretical analysis: sample construction, identification, asset-pricing tests, corporate-finance variables, robustness, code reproducibility, and evidence that can satisfy top-three-finance-journal standards. --- # Review of Finance Data Analysis Use this when the finance result is not yet credible enough for RoF. Reopen the current author guidelines and code-sharing policy before final submission. ## Audit - Map every finance claim to a table, figure, model result, identification test, or robustness check. - For empirical work, document sample construction, variable definitions, filters, winsorizing or trimming, identifiers, timing, and economic magnitudes. - For theory work, connect assumptions to equilibrium, comparative statics, or pricing implications that a broad finance audience can evaluate. - Use strong benchmarks: standard asset-pricing factors, corporate-finance controls, market microstructure alternatives, or banking/household-finance baselines as relevant. - Prepare code and data documentation early; RoF publication is conditional on receiving replication programs when the policy applies. ## Finance-result stress test For each headline result, record: ```text Result | Economic magnitude | Identification/model threat | Benchmark | Replication file ``` Then ask whether the claim would survive a top-three-finance referee: - Does the magnitude matter economically, not only statistically? - Is the benchmark the right one for asset pricing, corporate finance, banking, household finance, or market microstructure? - Are timing, sample filters, and variable construction strong enough to rule out mechanical effects? - For theory, do assumptions generate finance implications rather than only mathematical existence? - Can the code package reproduce the exact result and the robustness check that protects it? If the answer is weak, repair the analysis before rewriting the introduction. ## European and international data discipline RoF's EFA readership expects cross-market evidence handled with the same care US-only papers give CRSP. Known traps an RoF referee will catch: - Datastream/Refinitiv equity returns: remove padded post-delisting observations, stale repeated prices, and non-trading-day zeros; report screen-by-screen sample counts. - Delisting and survivorship: merge dead-firm lists back into Datastream samples and delisting returns into CRSP samples; a performance result built on survivors only is a mechanical effect, not a finding. - Bankscope/Orbis (Bureau van Dijk): consolidated and unconsolidated statements duplicate the same banking groups — filter on consolidation codes; record the database vintage, because BvD overwrites history and a later re-pull will not rebuild your sample. - Currency and fiscal timing: convert at observation-date exchange rates; align heterogeneous fiscal-year ends; lag accounting data enough to rule out look-ahead. - ECB SDW, Eurostat, and national-supervisor series: store exact series codes and download dates; revisions silently change merged regressors. ## Estimation defaults an RoF referee assumes | Setting | Expected default | Objection if absent | |---|---|---| | Firm/bank panel | firm + time FE; cluster by firm, two-way when shocks are common | "standard errors understated" | | Fama–MacBeth | Newey–West lags matched to horizon; Shanken correction | errors-in-variables attack | | Portfolio sorts | value-weighted headline plus equal-weighted check; microcap screen | "driven by tiny illiquid stocks" | | Staggered adoption | heterogeneity-robust DID alongside TWFE | negative-weights critique | | Cross-country panel | country-by-year FE or equivalent; cluster at country | "one country's shock in disguise" | | Anomaly/factor claim | multiple-testing discipline; international or out-of-sample split | data-snooping objection | Put one defining specification in the body and route the grid of variants to the internet appendix — RoF editors prize clean identification and economic magnitudes over kitchen-sink regressions. ## Worked vignette — a euro-area bank-margin paper Illustrative numbers only. The headline: after the 2014 negative-policy-rate cut, high-deposit banks lowered lending margins 28 bp more than low-deposit banks; sample of 412 euro-area banks from Bankscope, 2010–2019. - Magnitude: 28 bp on a 180 bp mean margin is roughly 16% — state it that way, not as a t-statistic. - Mechanical check: consolidation duplicates double-count the largest groups; rerun on unconsolidated statements. If 28 bp falls to 9 bp, the result is a filtering artifact. - Benchmark: contrast with matched non-euro banks to separate the negative-rate channel from the post-crisis trend; that contrast belongs in the body. - Replication hook: the Bankscope extract cannot ship; queue the pseudo-dataset and run logs now (see `rof-replication-and-data-policy`). ## Pipeline discipline from raw pull to exhibit One reproducible path, because RoF can hold publication until programs arrive: ```text raw/ immutable vendor pulls (Datastream, Bankscope, CRSP), dated build/ cleaning scripts: screens, delisting merges, winsorize 1/99 analysis/ one numbered script per manuscript table or figure out/ exhibits regenerated end-to-end by run_all; diffs reviewed ``` - Winsorize or trim ratio variables and state the percentile in every table note. - Fix seeds for any bootstrap or simulation inference and log them. - Maintain a variable dictionary mapping each manuscript symbol to its construction line. ## Output format ```text [Analysis readiness] strong / adequate / weak [Claim -> evidence] [Top-three-standard gap] [Replication blocker] [Next analysis] ```