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finance

Category: review
Field: economics
License: private (curator-owned)
Updated: 2026-05-20
Stages: referee-simulation

Curator-private skill — copy text from 100xOS/shared/skills/review/finance.md.

Finance Paper Review Checklist

Overview

Quality checklist for reviewing empirical finance papers. Covers methodology, data, presentation, and common pitfalls specific to financial economics research.

Data Quality

Sample Construction

  • Sample selection criteria clearly stated and justified.
  • Universe defined (CRSP, Compustat, specific exchange, country).
  • Filters documented: minimum price, minimum market cap, exclusion of financials/utilities, share type (ordinary common equity only).
  • Sample period justified. Long enough for reliable inference (typically 20+ years for asset pricing, 5+ years for corporate finance).
  • Number of observations, firms, and time periods reported.

Survivorship Bias

  • Includes delisted firms (not just survivors).
  • Uses delisting returns from CRSP (not just dropping firms at delisting).
  • If using a database that only covers survivors (e.g., some international databases), acknowledge the bias.

Look-Ahead Bias

  • Accounting data available to investors at portfolio formation (use lagged data: e.g., fiscal year-end data available 6 months later — June sorting convention).
  • No future information used in variable construction.
  • Real-time data vintages used for macro variables if relevant.

Data Cleaning

  • Outlier treatment documented (winsorization at 1/99% or trimming). Consistent across all variables.
  • Penny stocks excluded or results shown with and without (price > $1 or $5).
  • Micro-caps addressed (NYSE size breakpoints, or explicit exclusion of bottom quintile by ME).
  • Missing data handling explained (how are missing returns, missing accounting data treated?).

Methodology

Standard Errors

  • Appropriate standard errors for the setting:
  • Time-series regressions: Newey-West HAC (specify lag length).
  • Panel regressions: clustered by firm and/or time (Petersen 2009).
  • Fama-MacBeth: Newey-West on the time series of cross-sectional slopes (or Shanken correction).
  • Event studies: BMP or Kolari-Pynnonen.
  • Justification for clustering choice. Petersen (2009) guidance: cluster on the dimension with fewer clusters.
  • NOT using simple OLS standard errors for panel data with firm or time effects.

Return Computation

  • Log returns vs simple returns: stated and consistent. Log returns for time-series analysis; simple returns for portfolio aggregation.
  • Compounding method for multi-period returns: buy-and-hold (BHAR) vs cumulative abnormal (CAR). State which and why.
  • Excess returns: clearly defined (in excess of risk-free rate, typically 1-month T-bill).
  • Currency: returns in local currency or USD, and how currency effects are handled.

Factor Model Specification

  • Factor model choice justified (CAPM, FF3, FF5, q-factor, etc.).
  • Factor data source cited (Ken French library, AQR, self-constructed).
  • Results robust to alternative factor models (show at least two).
  • If self-constructed factors: construction methodology detailed and matches standard conventions.

Portfolio Construction

  • Sorting variable, breakpoints, and rebalancing frequency documented.
  • NYSE breakpoints used for CRSP sorts (avoids micro-cap domination).
  • Value-weighted returns as primary (equal-weighted as robustness — EW overweights micro-caps).
  • Holding period and skip-month convention for momentum documented.

Endogeneity and Identification

  • Potential endogeneity concerns discussed.
  • If causal claims made: identification strategy clearly stated (IV, DiD, RDD, natural experiment).
  • Instruments validated: relevance (first-stage F > 10) and exclusion restriction argued.
  • Reverse causality addressed.

Statistical Issues

Multiple Testing

  • If testing multiple hypotheses: acknowledge the problem.
  • Apply appropriate corrections (Bonferroni, Holm, FDR/Benjamini-Hochberg).
  • For new anomalies: use t > 3.0 threshold (Harvey-Liu-Zhu 2016) or bootstrap.
  • Pre-registration or holdout sample if feasible.

Economic vs Statistical Significance

  • Report economic magnitudes (basis points per month, percent per year, dollar amounts).
  • Discuss whether the effect is large enough to matter after transaction costs.
  • For trading strategies: account for bid-ask spreads, market impact, short-selling costs, and capacity constraints.
  • Sharpe ratio or information ratio for strategy performance.

Small Sample / Power

  • Is the sample large enough for the tests used?
  • For GRS tests: N (test assets) should be much less than T (time periods).
  • For Fama-MacBeth: at least 200+ months for reliable inference.
  • For event studies: sufficient events for cross-sectional tests (N > 30 minimum, ideally 100+).

Non-Stationarity

  • Unit root tests performed if using price levels, valuation ratios, or macro variables.
  • Stambaugh (1999) bias addressed if using persistent regressors in predictive regressions.
  • Regime changes or structural breaks considered (pre/post crisis, regulatory changes).

Presentation

Tables

  • Summary statistics table: N, mean, median, SD, min, max, percentiles for all key variables.
  • Correlation matrix for main variables.
  • Regression tables: coefficients, t-statistics (or standard errors), R^2, N, fixed effects indicators.
  • Portfolio sort tables: mean return, alpha, t-statistics, monotonicity pattern, high-minus-low spread.
  • Standard errors in parentheses, t-statistics in brackets — be consistent.
  • Significance stars: * p<0.10, ** p<0.05, *** p<0.01 (state convention).

Figures

  • Time series of key variables or cumulative returns.
  • Event study: cumulative average abnormal returns with confidence bands.
  • Portfolio performance: cumulative wealth plot.
  • Coefficient stability across subperiods or specifications (coefficient plot).

Reporting Conventions

  • Returns in percent (not decimals). State frequency (daily, monthly, annual).
  • Report annualized returns and volatilities where appropriate (multiply monthly by 12 and sqrt(12)).
  • Market cap in millions or billions. State currency and date for nominal values.
  • Book-to-market, not market-to-book (Fama-French convention).
  • Basis points (bps) for small return differentials (1 bp = 0.01%).

Common Pitfalls in Finance Papers

  1. Cross-sectional correlation: Fama-MacBeth addresses this, but pooled OLS with firm-clustered SEs does not account for time effects. Use double-clustering (Petersen 2009) or Fama-MacBeth.

  2. Microstructure noise: Using daily closing prices for high-frequency analysis introduces bid-ask bounce. Use midpoint prices or apply appropriate filters.

  3. Rebalancing assumptions: Monthly rebalanced portfolios assume zero transaction costs. Discuss turnover and implementation costs.

  4. Short-selling constraints: Long-short strategies assume costless shorting. Discuss short interest, lending fees, and institutional constraints.

  5. Data snooping: The finance "factor zoo" problem. New anomalies must clear a high bar. Pre-specify hypotheses.

  6. Stale prices: Illiquid stocks have stale prices that induce spurious predictability and autocorrelation. Use the Dimson (1979) beta correction.

  7. Penny stock bias: Many anomalies concentrate in micro-cap and penny stocks. Results should survive exclusion of stocks below $5 or below the 20th NYSE size percentile.

  8. Backfill bias: Databases may backfill historical data when a firm is added, creating upward bias in performance.

  9. Index inclusion effects: S&P 500 additions/deletions have well-documented price effects. Control for or exclude these events if they contaminate the sample.

  10. Seasonality: January effect, turn-of-month, day-of-week effects. Check if results are driven by calendar regularities.

Key References

  • Petersen, M.A. (2009). Estimating standard errors in finance panel data sets: Comparing approaches. Review of Financial Studies.
  • Harvey, C.R., Liu, Y., and Zhu, H. (2016). ... and the cross-section of expected returns. Review of Financial Studies.
  • Stambaugh, R.F. (1999). Predictive regressions. Journal of Financial Economics.
  • Hou, K., Xue, C., and Zhang, L. (2020). Replicating anomalies. Review of Financial Studies.
  • Fama, E.F. and French, K.R. (2008). Dissecting anomalies. Journal of Finance.
  • Cochrane, J.H. (2011). Presidential address: Discount rates. Journal of Finance.
  • McLean, R.D. and Pontiff, J. (2016). Does academic research destroy stock return predictability? Journal of Finance.