Next-session returns after a big one-day gain, by size of the gain
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 2026-09-27, from What Happens After a Big One-Day Gain.
| gain_bucket | observations | median_next_cc_pct | mean_next_cc_pct | median_next_oc_pct |
|---|---|---|---|---|
| under 10% | 639 | -0.27 | -0.15 | -0.23 |
| 10 to 15% | 2647 | -0.58 | -0.18 | -0.54 |
| 15 to 25% | 5689 | -1.1 | -0.64 | -0.82 |
| 25 to 50% | 4188 | -2.35 | -0.5 | -1.14 |
| 50% and up | 1817 | -9.66 | -5.85 | -2.32 |
- Rows × columns
- 5 × 5
- 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 |
|---|---|---|---|
gain_bucket |
text | 5 distinct values (10 to 15%, 15 to 25%, 25 to 50%…) | |
observations |
number | 639 to 5,689 | |
median_next_cc_pct |
number | -9.66 to -0.27 | percent |
mean_next_cc_pct |
number | -5.85 to -0.15 | percent |
median_next_oc_pct |
number | -2.32 to -0.23 | 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.
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 bars AS
(
SELECT
ticker,
date,
max(toFloat64(open)) AS o,
max(toFloat64(close)) AS c,
max(toFloat64(volume)) AS vol
FROM global_markets.stocks_daily_aggs
WHERE date >= today() - 1095
AND date <= today() - 2
AND ifNull(otc, 0) = 0
AND ticker NOT IN ('SPCX')
GROUP BY ticker, date
),
seq AS
(
SELECT
ticker,
date,
c,
vol,
lagInFrame(c) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN 1 PRECEDING AND 1 PRECEDING) AS prev_c,
leadInFrame(o) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN 1 FOLLOWING AND 1 FOLLOWING) AS next_o,
leadInFrame(c) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN 1 FOLLOWING AND 1 FOLLOWING) AS next_c,
leadInFrame(date) OVER (PARTITION BY ticker ORDER BY date ASC ROWS BETWEEN 1 FOLLOWING AND 1 FOLLOWING) AS next_date
FROM bars
),
movers AS
(
SELECT
date,
ticker,
100 * (c / prev_c - 1) AS gain_pct,
100 * (next_c / next_o - 1) AS next_oc_pct,
100 * (next_c / c - 1) AS next_cc_pct,
row_number() OVER (PARTITION BY date ORDER BY c / prev_c DESC, ticker ASC) AS rnk
FROM seq
WHERE prev_c >= 5
AND next_o > 0
AND next_c > 0
AND c * vol >= 5000000
AND dateDiff('day', date, next_date) <= 6
)
SELECT
multiIf(gain_pct < 10, 'under 10%',
gain_pct < 15, '10 to 15%',
gain_pct < 25, '15 to 25%',
gain_pct < 50, '25 to 50%',
'50% and up') AS gain_bucket,
count() AS observations,
round(quantileDeterministic(0.5)(next_cc_pct, cityHash64(ticker, toString(date))), 2) AS median_next_cc_pct,
round(avg(next_cc_pct), 2) AS mean_next_cc_pct,
round(quantileDeterministic(0.5)(next_oc_pct, cityHash64(ticker, toString(date))), 2) AS median_next_oc_pct
FROM movers
WHERE rnk <= 20
GROUP BY gain_bucket
ORDER BY min(gain_pct) ASC
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