Open Source Asset Pricing (Chen-Zimmermann)

Open Source Asset Pricing (Chen & Zimmermann 2022, RFS) is the free, reproducible cross-sectional anomaly dataset: 212 firm-level signals plus pre-computed long-short and decile portfolio returns, with the CRSP/Compustat merge already done. It is what the ZeroPaper pipeline uses to test whether a model’s mechanism maps to a known anomaly. This page is the distilled recipe.

• Cost: free, no auth.
• Coverage: 212 signals; original-paper and decile/quintile portfolios.
• Site: https://www.openassetpricing.com · Code: https://github.com/OpenSourceAP/CrossSection

Access

Option 1 — openassetpricing package (preferred)

# pip install openassetpricing
from openassetpricing import OpenAP
ap = OpenAP()

# Firm-level signals — predictor arg MUST be a list (see gotchas)
sig = ap.dl_signal("pandas", ["BM", "Mom12m", "AssetGrowth"])
#   → permno, yyyymm, BM, Mom12m, AssetGrowth

port = ap.dl_port("op", "pandas", ["BM"])      # original-paper portfolios
ap.list_port()                                  # op, deciles_ew/vw, quintiles_*
docs = ap.dl_signal_doc("pandas")               # all 212 with paper refs

Option 2 — Direct download (fallback)

A ~1.6 GB zipped wide CSV of all predictors is at https://www.openassetpricing.com/data/. Download once, cache under data/, never re-pull.

Gotchas (the ones that bite pipelines)

The reason to read this page rather than the repo README. Site and GitHub confirmed reachable on the date above; the package/data interface is as documented (not bulk-fetched here — the full set is ~1.6 GB).

• Predictor arguments must be LISTS, not strings. ["BM"], never "BM". Passing a string is the #1 failure and the error is not obvious.
• The bulk download is ~1.6 GB. dl_all_signals / the direct CSV will blow memory and time if pulled naively. Request only the signals you need; cache aggressively.
• The CRSP/Compustat merge is already done. Do not re-merge — signals are delivered at permno × yyyymm. Re-merging double-counts and misaligns.
• yyyymm is an integer, not a date — convert before joining to returns.
• Releases are versioned and periodic. State the release you used (latest tags have been updated as recently as late 2025); results drift across releases.
• Pre-built portfolios beat hand-rolled. Use dl_port deciles for long-short spreads rather than re-sorting — it matches the paper’s methodology and avoids look-ahead in the sort.

Key signals (10 of 212)

Signal	Description	Category
BM	Book-to-market	Value
Mom12m	12-month momentum (skip last month)	Momentum
AssetGrowth	Asset growth	Investment
GP	Gross profitability	Profitability
EP	Earnings-to-price	Value
Beta	CAPM beta	Risk
IdioVol	Idiosyncratic volatility	Risk
Accruals	Operating accruals	Quality
SUE	Standardized unexpected earnings	Earnings
ShareIss1Y	Net share issuance, 1yr	Issuance

ap.dl_signal_doc('pandas') for the full 212 with references.

Standard operations

• Long-short spread: decile 10 − decile 1 from dl_port("deciles_vw", …).
• Alpha: regress the long-short series on FF5 (see Ken French).
• Signal-zoo test: does your model’s mechanism map onto an existing anomaly, or is it genuinely new?
• Always state the signals, release/version, sample period, and weighting.

Citation

Chen, A. Y., and T. Zimmermann (2022). “Open Source Cross-Sectional Asset Pricing.” Review of Financial Studies. Data from https://www.openassetpricing.com, release [tag], accessed YYYY-MM-DD.