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This paper introduces a two-stage system for extracting and labeling events from SEC 8-K filings using a fine-grained taxonomy of 119 event types. By employing fuzzy n-gram validation to anchor tags to verbatim quotes and re-grading these quotes against category definitions, the system achieves a significant increase in tagging precision, with quality scores rising from 12% to 96%. The approach not only enhances the granularity of event classification but also demonstrates its economic relevance through an event study on abnormal returns, confirming the taxonomy's ability to distinguish between economically distinct events.
Tagging precision for SEC 8-K filings skyrockets from 12% to 96% with a novel two-stage system that grounds event labels in source text.
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 taxonomy of 119 event types. The first stage constrains output to valid taxonomy entries and anchors every tag to a verbatim quote via fuzzy n-gram validation; the second re-grades each cited quote against the category definition to produce a quality score. Applying the system to 292,984 filings from 2022 to 2026 yields 601,088 grounded event tags, which we release. Over 5,125 stratified tags, an LLM judge finds precision rises monotonically with the quality score, from 12% to 96%, while unsupported tags fall from 8% to near zero. Ablation shows the score is calibrated only when assigned in a dedicated second pass. An event study on unsigned abnormal returns confirms, without any language model, that the taxonomy separates economically distinct events sharing an item code.
