Pre-Submission Review for Finance Papers

Finance reviewers often ask:

• what financial decision, friction, risk, or mechanism does the paper explain?
• are variables constructed without look-ahead, survivorship, or sample-selection bias?
• does the empirical design support the causal or predictive claim?
• are returns, events, disclosures, institutions, or firm outcomes measured correctly?
• are robustness checks economically meaningful?
• does the paper address alternative explanations?
• is the magnitude economically important, not just statistically detectable?
• can code and data materials reproduce the core tables and figures?
• does the target journal match the paper's theory, data, and contribution size?

Finance papers live or die on the connection between evidence and economic mechanism.

In our pre-submission review work, finance manuscripts most often fail because the result is statistically interesting but not yet persuasive as finance.

Mechanism gap: the paper reports a return, valuation, disclosure, or corporate-policy pattern without a convincing financial channel.

Timing problem: the data construction risks look-ahead bias, event-window ambiguity, or stale information.

Variable construction opacity: factor, signal, exposure, abnormal return, ownership, liquidity, or disclosure variables are hard to reproduce.

Alternative explanation weakness: the paper does not address market conditions, selection, omitted risk, institutional constraints, or firm heterogeneity.

Code package risk: the results depend on a long pipeline that is not documented well enough for a data editor or reviewer to trust.

A useful review should identify the first objection a finance referee would raise about credibility or economic interpretation.

The Review of Financial Studies says it publishes significant new research in financial economics and requires authors to publicly release code underlying any published paper as a condition of publication. Its code and data sharing policy says accepted empirical, experimental, or computational papers must provide data, code, and documentation needed for independent reproduction, subject to stated exemptions.

The Journal of Financial Economics data and code policy expects authors, after acceptance, to upload data, programs, and detailed instructions to Mendeley Data. The Journal of Finance code-sharing policy also points authors toward replication materials, with exceptions for constrained data or proprietary code.

Those policies shape pre-submission readiness. A finance paper should not wait until acceptance to discover that its analysis pipeline is hard to explain.

Send the manuscript, target journal, data dictionary, code repository or archive, README, table and figure outputs, raw-data access notes, event definitions, variable-construction notes, model appendix, robustness files, institutional context, and prior referee comments if available.

If the paper uses WRDS, CRSP, Compustat, OptionMetrics, Refinitiv, Bloomberg, TRACE, regulatory filings, proprietary data, or web-scraped data, include source constraints and timing logic.

A useful finance pre-submission review should include:

• finance-contribution verdict
• mechanism and theory critique
• data-construction and timing check
• identification and robustness review
• economic-magnitude interpretation
• code and data package readiness note
• journal-lane recommendation
• submit, revise, retarget, or diagnose deeper call

The review should not only say "add robustness." It should identify the alternative explanation or construction risk that will matter most to finance reviewers.

Before submission, authors often need to:

• rewrite the contribution around a finance mechanism
• clarify event timing and information availability
• document variable construction and sample filters
• separate prediction, association, and causal claims
• add risk-adjusted or institutionally meaningful robustness
• test the strongest alternative explanation
• clean code and map outputs to manuscript tables
• retarget from a top-three finance journal to a field or specialty finance journal when the claim is narrower

These fixes make the paper easier to evaluate and harder to dismiss quickly.

An asset-pricing paper needs a clear signal, factor, risk or mispricing story, timing discipline, transaction-cost context, and out-of-sample restraint. A corporate-finance paper needs credible firm decision logic, governance or incentive context, and identification discipline. A banking paper needs regulatory, balance-sheet, and institutional detail. A market-microstructure paper needs trading mechanism and data-frequency clarity. A fintech paper needs product, platform, regulatory, and adoption context. A household-finance paper needs behavior, sample-selection, and external-validity discipline.

The AI manuscript review can flag whether the blocking risk is mechanism, data construction, identification, code readiness, or journal fit.

Use this page when the manuscript's submission risk depends on finance-specific data, mechanisms, institutions, and audiences. Use economics review when the paper is mainly about broader economic behavior, labor, public policy, development, macro, IO, econometrics, or welfare analysis.

That distinction keeps the page focused on the finance buyer's actual problem.

Do not submit a finance paper if the main result depends on a variable construction or timing choice that is hard to audit. Finance reviewers will inspect whether the information was actually available when the paper says it was, whether the sample creates survivorship or selection problems, and whether the result survives plausible alternative construction.

Also pause if the manuscript treats statistical significance as enough. Finance papers need economic magnitude and mechanism. A small coefficient can matter if the institution, decision, or asset-pricing channel is strong. A large coefficient can still fail if no one can explain why it should exist.