gap_percentiles
Answered against 22 years of US equities and 12 years of US options data and published with the query that produced it. This result is stored as of 2026-10-03, from how-stock-splits-are-announced.
| percentile | gap_days |
|---|---|
| p10 quickest | 9 |
| p25 | 14 |
| p50 median | 40 |
| p75 | 170 |
| p90 slowest | 295 |
- Rows × columns
- 5 × 2
- Computed
- Completeness
- No missing values
- Source
- US exchange, SIP and OPRA market data
- Licence
- Strasmore terms · free, no signup
What each column holds
| Column | Type | Range | Notes |
|---|---|---|---|
percentile |
text | 5 distinct values (p10 quickest, p25, p50 median…) | |
gap_days |
number | 9 to 295 |
Computed from Strasmore's warehouse of US exchange, SIP and OPRA market data. Equity prices are delayed; options greeks and implied volatility are end-of-day. This result is stored, not recomputed on load — it is exactly the numbers that were returned on , and the query below is what returned them.
Run it yourself
This is the exact query behind the result above. Change a ticker, a date or a column and run it against the warehouse — no account, no key. The no-signup tier is smaller than the one this page was computed on; a query that reaches past it comes back saying which plan runs it.
WITH
split_events AS (
SELECT
ticker,
execution_date,
max(toFloat64(split_to)) AS to_shares,
max(toFloat64(split_from)) AS from_shares
FROM global_markets.stocks_splits
WHERE execution_date >= today() - 730
AND execution_date < today()
AND toFloat64(split_to) > toFloat64(split_from)
AND ticker NOT IN ('SPCX')
GROUP BY ticker, execution_date
),
split_stories AS (
SELECT
arrayJoin(tickers) AS story_ticker,
toDate(published_utc) AS story_date
FROM global_markets.stocks_news
WHERE published_utc >= today() - 1140
AND positionCaseInsensitive(title, 'split') > 0
),
gaps AS (
SELECT
e.ticker AS ticker,
e.execution_date AS execution_date,
dateDiff('day', min(s.story_date), e.execution_date) AS gap_days
FROM split_events AS e
INNER JOIN split_stories AS s ON s.story_ticker = e.ticker
WHERE s.story_date < e.execution_date
AND s.story_date >= e.execution_date - 400
GROUP BY e.ticker, e.execution_date
)
SELECT
p.1 AS percentile,
p.2 AS gap_days
FROM
(
SELECT arrayJoin([
('p10 quickest', toUInt32(round(quantileDeterministic(0.10)(toFloat64(gap_days), cityHash64(ticker, execution_date))))),
('p25', toUInt32(round(quantileDeterministic(0.25)(toFloat64(gap_days), cityHash64(ticker, execution_date))))),
('p50 median', toUInt32(round(quantileDeterministic(0.50)(toFloat64(gap_days), cityHash64(ticker, execution_date))))),
('p75', toUInt32(round(quantileDeterministic(0.75)(toFloat64(gap_days), cityHash64(ticker, execution_date))))),
('p90 slowest', toUInt32(round(quantileDeterministic(0.90)(toFloat64(gap_days), cityHash64(ticker, execution_date)))))
]) AS p
FROM gaps
)
Work with this data in your AI assistant
Opens ready to query, with this page's data. Free, no account.