These files are model OUTPUTS and sample-definition indices derived from Compustat via WRDS under The University of Texas at Austin's academic subscription. They are released under the Forma Non-Commercial Research Licence (WRDS-Conditioned) v1.0 — see LICENSE.md. Access requires that you hold your own current Compustat/WRDS licence, use the files for non-commercial academic research only, and do not redistribute them. No raw Compustat values are distributed here; rebuilding the benchmark data requires your own WRDS access, which you need in order to use these artifacts at all.

ProForma-20Q evaluation artifacts

The artifacts needed to reproduce the paper's pooled evaluation without regenerating every model: the scored forecast files in standardized space, the sample-coverage mask that defines the common evaluation footprint, and the per-horizon calibration series.

This complements — it does not replace — the benchmark package. The pipeline, task definition, evaluation code, and one-command WRDS rebuild live in proforma-20q (Apache-2.0).

These forecasts are in the regularized (asinh z-score) target space, which is what the evaluator scores. If you want dollars, use forma-usd-forecasts instead — do not try to interpret these values as currency.

file	what	size
`forecasts/forma_fgrid__pf_full__test__predictions.parquet`	Forma Gaussian 5-seed mixture — the squared-error / point track (Panel A)	3.98 GB
`forecasts/forma_lap05_fgrid__pf_full__test__predictions.parquet`	Forma Laplace 5-seed mixture — the absolute-error track (Panel B)	7.99 GB
`forecasts/forma_lap05_fgrid__pf_full__test__predictions.nll.json`	density-family sidecar for the above — see the warning below	<1 KB
`mask/full_sample_mask_bits.npy`	the 327,244,429-cell Full-sample coverage bitmap (grid-aligned packbits; no firm identifiers)	69 MB
`mask/full_sample_grid_rows.parquet`	canonical row index `(grid_row, firm, origin)` the bitmap is a bitmap over	0.9 MB
`calibration/coverage_by_horizon.csv`	Forma Gaussian per-horizon PIT, z², CRPS and 50/80/90/95% interval coverage	3 KB
`calibration/*_calibration/coverage_by_horizon.csv`	the same series for the Laplace, FFNN and Chronos comparators	~3 KB each

The calibration series ship for every model in the figure, including ones whose forecast files are not hosted here: they are results the paper promises (§5.2, "per-horizon series in the release"), and they are kilobytes, not gigabytes.

⚠️ Keep the .nll.json sidecar next to its parquet. The evaluator resolves the density family from {stem}.nll.json and silently defaults to Gaussian when it is absent. A Laplace forecast scored as Gaussian produces plausible, wrong numbers with no error. If you move or rename the Laplace parquet, move the sidecar with it.

Why the mask ships as a bitmap plus a row index

The paper's headline numbers are computed on the intersection of every model's finite predictions with the truth — 327.2M cells for the Full sample. Recomputing that intersection requires every model's forecasts, which is a multi-hundred-GB download nobody wants. The bitmap encodes membership directly.

full_sample_grid_rows.parquet is what makes the bitmap portable. It is the canonical (grid_row, firm, origin) index the bits are indexed against, so the mask still applies to a rebuild from a different Compustat vintage, matched by (firm, origin) value rather than by position. It is 0.9 MB and it cannot be derived from the forecasts — the canonical grid contains rows no model ever forecast. Pass both:

proforma20q evaluate my_forecasts.parquet --sample-mask full_sample_mask_bits.npy \
                                          --grid-rows full_sample_grid_rows.parquet

What is deliberately not here

No Compustat records. The truth panel is rebuilt from your own WRDS pull with the benchmark package; that is why a WRDS licence is a precondition rather than a formality.
No dollar values. See forma-usd-forecasts.
No competitor forecasts. The paper promises seeded regeneration scripts for the learned competitors, not their outputs. The FFNN, Chronos, chained GBM, naive, ElasticNet and random-forest forecasts are all regenerated from the Apache-2.0 repositories rather than hosted here — that is ~19 GB of storage buying nothing the paper committed to.
No regularization statistics. Those live once, with the model, in forma-lab-mccombs/forma, so there is a single source of truth for the (μ, σ, k) that define the target space.

Provenance

These files are mirrored byte-for-byte from the archival deposit at 10.5281/zenodo.21269003 and carry the same md5 checksums, so proforma-20q's pinned digests verify against either source.


Benchmark package, builder, evaluation code	`proforma-20q` (Apache-2.0)
Model code, configs, competitor code	`forma-release` (Apache-2.0)
Trained weights + regularization statistics	forma-lab-mccombs/forma
Forecasts in USD ($M)	forma-lab-mccombs/forma-usd-forecasts

Licence

Forma Non-Commercial Research Licence (WRDS-Conditioned) v1.0 — see LICENSE.md. Non-commercial academic research only; you must hold your own current Compustat/WRDS licence; no redistribution; attribution required.

All commercial rights are reserved to The University of Texas at Austin. For commercial licensing, contact UT Discovery to Impact (ip@discoveries.utexas.edu).

Derived from Compustat under UT Austin's academic subscription. S&P Global Market Intelligence retains all rights in the underlying data. Nothing here is investment advice.

Citation

Cite the companion paper; see CITATION.cff in forma-release.

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