arXiv:2607.08346v1 Announce Type: new Abstract: Form 8-K filings are the primary channel through which U.S. public companies disclose material events, but the SEC item codes attached to them are coarse: a single item spans routine administrative changes and chief executive departures, and many of the most market-moving disclosures fall into a catch-all item. Large language models make fine-grained labelling feasible at corpus scale, but only if the labels can be traced to the source text and shown to be reliable. We present a two-stage system that tags 8-K disclosures against a three-tier taxo
The proliferation of powerful large language models makes fine-grained textual analysis feasible at scale, addressing a long-standing need for more detailed corporate event classification.
Improved granular event classification from SEC filings can provide more precise, actionable intelligence for financial markets, regulatory oversight, and business strategy.
The ability to automatically extract and categorize corporate events beyond coarse SEC codes will enhance market transparency and enable more sophisticated predictive analytics.
- · Financial analysts
- · Quantitative traders
- · Regulatory bodies
- · AI/NLP developers
- · Companies relying on ambiguity in disclosures
- · Manual data extractors
Financial markets will gain faster and more precise insights into corporate activities and material events.
This improved transparency could lead to more efficient market pricing and potentially reduce informational arbitrage opportunities.
The methodology might be adapted to other regulatory filings globally, creating a new standard for automated compliance and market surveillance.
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Read at arXiv cs.CL