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metadata
license: other
license_name: forma-nc-1.0
license_link: LICENSE.md
language:
  - en
tags:
  - finance
  - accounting
  - financial-statement-forecasting
  - probabilistic-forecasting
  - time-series-forecasting
  - forma
  - proforma-20q
pretty_name: Forma  pro-forma financial statement forecaster
extra_gated_prompt: >-
  Released under the Forma Non-Commercial Research Licence v1.0 (see
  LICENSE.md). Non-commercial academic research only; 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). No Compustat records are distributed and
  firm-level Compustat values cannot be reconstructed from these weights, so no
  WRDS licence is required to download them; reproducing the training or
  evaluation data does require your own.
extra_gated_fields:
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extra_gated_button_content: Accept licence and download

Forma — pro-forma financial statement forecaster

Forma jointly forecasts the complete quarterly financial statements of U.S. public firms at horizons of 1–20 quarters, emitting a predictive distribution per statement item.

This repository holds the trained weights and the artifacts required to run or score them. The model code is separate and Apache-2.0: forma-lab-mccombs/forma-release.

Contents

file what
checkpoints/forma_fgrid_seed{60..64}.ckpt Gaussian β-NLL fleet, 5 seeds (11.4 MB each) — the squared-error / point track
checkpoints/forma_lap05_fgrid_seed{60..64}.ckpt Laplace fleet, 5 seeds — the absolute-error track
reference/regularization_stats.parquet per-(account, quarter) μ / σ / k — required to encode inputs or decode outputs
reference/account_id_map.csv account name ↔ id; the account embedding is indexed by this, so it must match training

The weights are inert without the regularization statistics. Forma predicts a per-account, per-quarter asinh z-score; without (μ, σ, k) you can neither build inputs nor interpret outputs. They ship here for that reason.

Training configurations live in the Apache-2.0 code repository (forma-release), not here. No firm-identifier map is distributed: Forma consumes no firm identity — there is no firm embedding — so none is needed to run the model.

Model

Gaussian β-NLL, transformer-only (no constraint-correction layer, no reconciliation), ~1M parameters. Trained on Compustat quarterly filings 1971–2001, validated 2002–2009, tested 2010+. Universal forecaster: observable accounting and industry data only, no firm-specific identifiers.

The released forecasts are the 5-seed equal-weight mixture of the Gaussian fleet, not any single seed.

Intended use and limitations

Academic research on financial-statement forecasting. Not investment advice; see LICENSE.md §9.

  • Calibration. The 90% interval is close to nominal (0.905–0.939 measured); the central 50% interval is empirically over-wide (~0.58–0.69 vs 0.50).
  • Reg stats stop at 2024Q4. Applying Forma to later data requires re-estimating (μ, σ, k) from your own Compustat pull — the builder is in the Apache repo. Re-estimated statistics define a different target space, and scores computed against them are not comparable to published numbers.
  • Financial firms are excluded (SIC 6000–6999).
  • Dollar-space skew. If you back-transform to dollars, note that a Gaussian in z-space is lognormal-tailed in dollars: use quantiles, and treat the predictive standard deviation as unusable in the tail. See the USD forecast dataset card.

Related

Model code, configs, competitors forma-release (Apache-2.0)
Benchmark package + builder proforma-20q (Apache-2.0)
Forecasts in USD ($M) forma-lab-mccombs/forma-usd-forecasts (WRDS-conditioned, gated)
Standardized forecasts + sample mask forma-lab-mccombs/proforma-20q-artifacts (WRDS-conditioned, gated)

Licence

Forma Non-Commercial Research Licence v1.0 — see LICENSE.md. Non-commercial academic research only; 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).

Downloading the weights does not require a WRDS/Compustat licence: no Compustat records are distributed here and firm-level values cannot be reconstructed from ~1M model parameters. Rebuilding the training or evaluation data does require your own WRDS access — see the benchmark repo.

Some of these weights were previously distributed under Apache-2.0. That grant is irrevocable as to copies already obtained; this licence governs materials obtained here and all subsequent versions (LICENSE.md §10).

Derived from Compustat under UT Austin's academic subscription. S&P Global Market Intelligence retains all rights in the underlying data.

Citation

See CITATION.cff in forma-release.