Models · NLP / Investing

Sentiment from Filings

Extracts forward-looking sentiment, risk language, and management tone from 10-K, 10-Q, 8-K, and equivalent EU/UK filings.

  • Risk: low
  • GA
  • US
  • UK
  • EU
F1 (binary tone)
0.91
Backtest IR (2018-2025)
0.78
Median p99 latency
120 ms
Calls served
12.4 M

What it does

sentiment-from-filings ingests the full text of a regulatory filing and returns:

  • A scalar sentiment score (-1.0 to +1.0) for the document and per section.
  • A list of detected risk-language phrases with their location offsets.
  • A management-tone vector (confidence, hedging, novelty) for use as a feature in downstream models.

Where it shines

Long-only equity funds running event-driven strategies on earnings releases and 8-Ks. Backtests on 2018–2025 US large-cap show a 0.78 information ratio when used as a single signal in a market-neutral overlay; full methodology in our research notes.

Where to be careful

Sentiment models are reflexive. Crowded usage erodes the signal. Don’t size positions purely on this output — combine with at least one orthogonal signal.

How it’s trained

Trained on 25 years of EDGAR filings + Companies House filings, fine-tuned on a hand-labelled tone corpus. Re-trained quarterly; all training-data provenance hashes are published in the audit log.

Pricing

Included in Starter and above for up to plan-tier inference calls; metered overage at $0.001 / call.

Audit & jurisdiction

  • US — reviewed and routed to local counsel.
  • UK — reviewed and routed to local counsel.
  • EU — reviewed and routed to local counsel.

Every run of this model is appended to a hash-chained audit log. Anyone with a run id can fetch a Merkle inclusion proof — see the Compliance & Trust page.

Reported metrics are self-attested by the model author and verifiable against the audit log.