Event Taxonomies for Financial Text

A sentiment score alone can tell you a headline sounds negative, but it can't tell you why — and "why" matters enormously for what a trader should do next. A downgrade because of weak demand calls for a different response than a downgrade because of a one-off accounting restatement, and both are different again from an activist investor pushing for a breakup. Free-text news carries all of this, but a strategy can't condition its logic on unstructured prose. It needs the story reduced to a small, fixed set of labels it can actually branch on.

An event taxonomy is that fixed vocabulary: a list of mutually-ish exclusive categories — earnings release, guidance revision, M&A announcement, litigation, management change, credit-rating action, product recall, regulatory action, and so on — into which every relevant story gets sorted, usually alongside a sub-label for direction (positive/negative/neutral).

Building and using the taxonomy

Two design choices drive most of the value:

• Granularity. Too coarse (just "corporate news") loses the information that made structuring worthwhile. Too fine (hundreds of micro-categories) makes each bucket too sparse to backtest reliably or to collect enough labeled training examples for. Most production taxonomies settle on 15–40 top-level categories, sometimes with one layer of sub-types.
• Classification method. Small taxonomies with clear keyword signatures (e.g., "bankruptcy," "chapter 11") can be tagged with rule-based matching. Larger or more nuanced ones use a supervised text classifier trained on labeled examples, often bootstrapped with Weak Supervision for Financial Text Labels since hand-labeling enough events for every category is expensive.

Once a story is tagged, each event category typically has its own historically-estimated market impact — a guidance cut has a different average price reaction and decay profile than a routine executive resignation — so the taxonomy label becomes a feature that conditions how a downstream signal weights and decays the news.

Worked example

A desk defines a six-category taxonomy: Earnings, Guidance Change, M&A, Legal/Regulatory, Management Change, Other. A backtest of one year of tagged news for a single stock finds average five-day abnormal returns following each event type: Earnings surprises average $\pm 2.1\%$, Guidance Changes average $\pm 3.4\%$ (more idiosyncratic informational content, historically the most volatile category), M&A announcements average $\pm 8.9\%$ (largest single-story impact, but rare), and Management Change events average only $\pm 0.4\%$ (routine, absorbed quickly). Without the taxonomy, a strategy averaging across all "negative news" would blend a routine CFO departure with a guidance collapse into one meaningless number; with it, the strategy can size its response by category, weighting a rare M&A event completely differently from a routine earnings beat.

What this means in practice

An event taxonomy is what makes news a structured feature rather than a vague sentiment blob — it lets a strategy respond differently to a bankruptcy filing than to a routine buyback announcement, and it makes backtesting interpretable, since you can report performance broken down by event type instead of one undifferentiated "news signal." The taxonomy also has to be maintained: new event types emerge (a new SEC disclosure rule, a novel deal structure) and stale categories should be pruned, or classification accuracy silently degrades as the world drifts from the labeled training set.