STRASMORE/EXPLORE 2,170 QUERIES

After the split: median forward returns for 2025's whole-number forward splits vs SPY over the same sessions

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 2026-07-26, from What Is a Stock Split? Ratios and Mechanics.

as of scalar 1×5read in context →
splits measured
44
median 5 session pct
-0.9
median 21 session pct
-1.5
pct up after 21 sessions
45
median spy 21 session pct
1.6
Rows × columns
1 × 5
Computed
Completeness
No missing values
Source
US exchange, SIP and OPRA market data
Licence
Strasmore terms · free, no signup
Formats
JSON · CSV · the SQL below

What each column holds

Column definitions for After the split: median forward returns for 2025's whole-number forward splits vs SPY over the same sessions, derived from the stored result.
ColumnTypeRangeNotes
splits_measured number every row is 44
median_5_session_pct number every row is -0.9 percent
median_21_session_pct number every row is -1.5 percent
pct_up_after_21_sessions number every row is 45 percent
median_spy_21_session_pct number every row is 1.6 percent

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.

the exact SQL behind every number
WITH events AS (
    SELECT ticker, execution_date
    FROM global_markets.stocks_splits
    WHERE execution_date >= '2025-01-01' AND execution_date <= '2025-12-31'
      AND adjustment_type = 'forward_split' AND split_from = 1
      AND split_to >= 2 AND split_to = round(split_to)
),
daily AS (
    SELECT ticker,
           toDate(toTimeZone(window_start, 'America/New_York')) AS d,
           argMaxIf(toFloat64(close), window_start,
                    (toHour(toTimeZone(window_start, 'America/New_York')) * 60 + toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959) AS close_rth
    FROM global_markets.delayed_stocks_minute_aggs
    WHERE ticker IN (SELECT ticker FROM events UNION ALL SELECT 'SPY')
      AND window_start >= '2025-01-01 00:00:00' AND window_start < '2026-03-01 00:00:00'
    GROUP BY ticker, d
    HAVING close_rth > 0
),
series AS (
    SELECT ticker, arraySort(x -> x.1, groupArray((d, close_rth))) AS dc
    FROM daily
    GROUP BY ticker
),
spy AS (
    SELECT dc AS spy_dc FROM series WHERE ticker = 'SPY'
),
per_event AS (
    SELECT e.ticker AS tkr,
           arrayFirstIndex(x -> x.1 >= e.execution_date, s.dc) AS idx,
           arrayFirstIndex(x -> x.1 >= e.execution_date, spy_dc) AS sidx,
           s.dc AS dc, spy_dc
    FROM events e
    JOIN series s ON s.ticker = e.ticker
    CROSS JOIN spy
),
measured AS (
    SELECT tkr,
           (dc[idx + 5].2 / dc[idx].2 - 1) * 100 AS r5,
           (dc[idx + 21].2 / dc[idx].2 - 1) * 100 AS r21,
           (spy_dc[sidx + 21].2 / spy_dc[sidx].2 - 1) * 100 AS spy21
    FROM per_event
    WHERE idx > 0 AND length(dc) >= idx + 21
      AND sidx > 0 AND length(spy_dc) >= sidx + 21
      AND dc[idx].2 > 0
)
SELECT count() AS splits_measured,
       round(quantileDeterministic(0.5)(r5, cityHash64(tkr)), 1) AS median_5_session_pct,
       round(quantileDeterministic(0.5)(r21, cityHash64(tkr)), 1) AS median_21_session_pct,
       round(100.0 * countIf(r21 > 0) / count(), 0) AS pct_up_after_21_sessions,
       round(quantileDeterministic(0.5)(spy21, cityHash64(tkr)), 1) AS median_spy_21_session_pct
FROM measured

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