The problem. Reproduction dies at the data step more often than at any other — not because data doesn’t exist, but because papers describe it vaguely (“daily US equity data”), access it institutionally (WRDS subscriptions), or used it under licenses you can’t inherit. This guide is the location layer: what the standard datasets are, where they live, and what substitutes are legitimate when the original is out of reach.
The standard-dataset zoo
A surprising share of the literature runs on a small set of recurring datasets — recognize them on sight:
- CRSP / Compustat (via WRDS): the substrate of US asset-pricing research — survivorship-clean equities and point-in-time-ish fundamentals. Access: academic affiliation (cheap through a university library) or institutional money. No free substitute matches its delisting coverage.
- TAQ: US trades and quotes, the microstructure workhorse. Institutional. Modern papers increasingly use exchange-native ITCH data instead — even less accessible, more precise.
- FI-2010: the benchmark LOB dataset — free, downloadable, and the reason dozens of deep-LOB papers are comparable at all. Know its quirks: z-scored prices (mid-price reconstruction is painful — a problem we hit ourselves), one week, ten Finnish stocks. Results on FI-2010 are results about FI-2010.
- Ken French’s data library: factor returns and sorted portfolios, free, the standard benchmark layer for factor papers.
- Option surfaces (OptionMetrics): the options-research standard; institutional. Free substitutes are thin — this is the hardest genre to reproduce cheaply.
- Crypto: the happy exception — exchange APIs give full public history free, which is why crypto papers are the most reproducible genre in the archive.
Decoding a vague data section
When the paper doesn’t name its source, triangulate: the citation trail (data citations hide in footnotes and acknowledgments — “we thank X for providing…” is a data source); the summary-statistics fingerprint (universe size per year, date ranges, and mean returns narrow candidates fast — matching these is stage 4 of reproduction anyway); the appendix of a later paper (replications and follow-ups often document the original’s data more precisely than the original did); and the authors — a short, specific email asking “which vendor and which adjustment settings?” gets answered more often than cynics expect, especially within a year of publication.
The access-tier reality
| Tier | What it unlocks | Cost reality |
|---|---|---|
| Free (arXiv-adjacent) | FI-2010, French library, crypto APIs, FRED, some Kaggle mirrors | $0 — but mirrors are unversioned; verify against summary stats |
| Individual practitioner | Daily/intraday prices via API vendors | $30–500/mo — covers price-based reproduction |
| Academic | WRDS: CRSP, Compustat, TAQ, OptionMetrics | University affiliation; the cheapest legitimate path to research-grade history |
| Institutional | Exchange-native feeds, PIT fundamentals at scale | Four figures monthly and up |
The practical implication for independents: price-based papers are reproducible; fundamentals- and options-based papers usually require the academic tier. Factor that into which papers you choose to replicate.
Substitution rules — when exact data is unreachable
Substituting data changes the experiment; do it honestly:
- ☐ Match the properties that drive the result, not the label: frequency, universe breadth, survivorship handling, adjustment policy.
- ☐ Reconcile summary statistics between substitute and original before comparing results — divergence here explains divergence everywhere.
- ☐ Reframe your conclusion: “the effect replicates on Polygon data 2015–2025” is a generalization test, not a reproduction — arguably more valuable, but a different claim.
- ☐ Never substitute silently in a write-up; the substitution is a finding.
Questions to ask of any paper’s data
- Could I buy this data today, at which tier?
- Is the dataset one of the standard zoo (comparable results exist) or bespoke (nothing to benchmark against)?
- What does the choice of dataset conveniently exclude — dead names, a crisis, an asset class?
- If it’s a public benchmark like FI-2010: is the paper state-of-the-art on the benchmark, or on the problem?
How our scoring reflects data accessibility
Data transparency feeds the rigor score — named sources, date ranges, and construction rules score above “proprietary dataset” every time, and the paper pages surface each paper’s data description in the abstract and flowchart. What the score deliberately doesn’t do is penalize institutional data (TAQ-based work can be superb); it penalizes vagueness. Accessibility is your constraint to apply — via the tier table above.
The chain continues: find the code → · reproduce it → · Data infrastructure: vendor guide →