Forward price change after record closes vs all other sessions, SPY
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 2026-11-01, from Investing at All-Time Highs: What the Data Says.
| horizon | record_closes | other_closes | record_median_pct | other_median_pct | record_positive_pct | other_positive_pct |
|---|---|---|---|---|---|---|
| 1 year | 438 | 4127 | 12.7 | 13.7 | 78.8 | 82.7 |
| 3 years | 343 | 3718 | 26.8 | 38.7 | 99.4 | 93.6 |
| 5 years | 327 | 3230 | 66.3 | 75 | 99.4 | 97.6 |
- Rows × columns
- 3 × 7
- 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 |
|---|---|---|---|
horizon |
text | 3 distinct values (1 year, 3 years, 5 years) | |
record_closes |
number | 327 to 438 | |
other_closes |
number | 3,230 to 4,127 | |
record_median_pct |
number | 12.7 to 66.3 | percent |
other_median_pct |
number | 13.7 to 75 | percent |
record_positive_pct |
number | 78.8 to 99.4 | percent |
other_positive_pct |
number | 82.7 to 97.6 | 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.
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
daily AS
(
SELECT
date,
toFloat64(argMax(close, _ingest_time)) AS close
FROM global_markets.stocks_daily_aggs
WHERE ticker = 'SPY'
GROUP BY date
),
flagged AS
(
SELECT
date,
close,
close >= max(close) OVER (ORDER BY date ROWS BETWEEN UNBOUNDED PRECEDING AND CURRENT ROW) AS is_record,
min(date) OVER () AS series_start,
leadInFrame(close, 252) OVER (ORDER BY date ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) AS close_1y,
leadInFrame(close, 756) OVER (ORDER BY date ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) AS close_3y,
leadInFrame(close, 1260) OVER (ORDER BY date ROWS BETWEEN CURRENT ROW AND UNBOUNDED FOLLOWING) AS close_5y
FROM daily
),
unpivoted AS
(
SELECT
date,
close,
is_record,
arrayJoin([('1 year', close_1y), ('3 years', close_3y), ('5 years', close_5y)]) AS pair,
tupleElement(pair, 1) AS horizon,
tupleElement(pair, 2) AS close_fwd
FROM flagged
WHERE date >= addYears(series_start, 4)
)
SELECT
horizon,
countIf(is_record = 1) AS record_closes,
countIf(is_record = 0) AS other_closes,
round(quantileDeterministicIf(0.5)(100 * (close_fwd / close - 1), toYYYYMMDD(date), is_record = 1), 1) AS record_median_pct,
round(quantileDeterministicIf(0.5)(100 * (close_fwd / close - 1), toYYYYMMDD(date), is_record = 0), 1) AS other_median_pct,
round(100 * countIf(is_record = 1 AND close_fwd > close) / countIf(is_record = 1), 1) AS record_positive_pct,
round(100 * countIf(is_record = 0 AND close_fwd > close) / countIf(is_record = 0), 1) AS other_positive_pct
FROM unpivoted
WHERE close_fwd > 0
GROUP BY horizon
HAVING countIf(is_record = 1) > 0 AND countIf(is_record = 0) > 0
ORDER BY horizon
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