Survivors-only average vs whole-cohort average, by starting year
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 2026-08-22, from Survivorship Bias in Stock Data, Explained.
| cohort_year | cohort_size | gone_count | survivors_only_pct | full_universe_pct | gap_pct |
|---|---|---|---|---|---|
| 2016 | 2728 | 969 | 209.9 | 148.2 | 61.8 |
| 2017 | 2522 | 819 | 160.8 | 117.3 | 43.4 |
| 2018 | 2669 | 824 | 121.4 | 88.7 | 32.6 |
| 2019 | 2646 | 695 | 138.1 | 112.7 | 25.5 |
| 2020 | 2669 | 639 | 87 | 73.1 | 13.9 |
| 2021 | 3416 | 957 | 64.4 | 48.4 | 16 |
| 2022 | 3378 | 694 | 34.2 | 24.9 | 9.2 |
- Rows × columns
- 7 × 6
- 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 |
|---|---|---|---|
cohort_year |
text | 7 distinct values (2016, 2017, 2018…) | |
cohort_size |
number | 2,522 to 3,416 | |
gone_count |
number | 639 to 969 | count |
survivors_only_pct |
number | 34.2 to 209.9 | percent |
full_universe_pct |
number | 24.9 to 148.2 | percent |
gap_pct |
number | 9.2 to 61.8 | 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
entry AS
(
SELECT
ticker,
toYear(date) AS cohort_start,
argMin(toFloat64(close), date) AS entry_close
FROM global_markets.stocks_daily_aggs
WHERE toMonth(date) = 1
AND date BETWEEN '2016-01-01' AND '2022-01-31'
AND ticker NOT IN ('SPCX')
GROUP BY ticker, cohort_start
HAVING argMin(toFloat64(close), date) >= 5
AND avg(volume) >= 250000
),
outcome AS
(
SELECT
ticker,
argMax(toFloat64(close), date) AS final_close,
max(date) AS last_bar
FROM global_markets.stocks_daily_aggs
WHERE date >= '2016-01-01'
GROUP BY ticker
)
SELECT
toString(e.cohort_start) AS cohort_year,
count() AS cohort_size,
countIf(o.last_bar < today() - 45) AS gone_count,
round(100 * avgIf(o.final_close / e.entry_close - 1, o.last_bar >= today() - 45), 1) AS survivors_only_pct,
round(100 * avg(o.final_close / e.entry_close - 1), 1) AS full_universe_pct,
round(100 * (avgIf(o.final_close / e.entry_close - 1, o.last_bar >= today() - 45)
- avg(o.final_close / e.entry_close - 1)), 1) AS gap_pct
FROM entry AS e
INNER JOIN outcome AS o ON o.ticker = e.ticker
GROUP BY e.cohort_start
HAVING countIf(o.last_bar >= today() - 45) > 0
ORDER BY e.cohort_start
Run your own version of this
The same 22 years of US equities and 12 years of options data are queryable in SQL or plain English. A free account runs 100 queries a day and takes no card.
More from this analysisSurvivorship Bias in Stock Data, Explained
Symbols that printed a final daily bar, by year
ranking 10×3
→
The January 2019 universe, grouped by what happened to each name
ranking 9×4
→
Symbols relisted under a new issuer after a long silence
ranking 5×4
→
Survivorship in the universe: names trading each year, share still listed in July 2026, and median return
table 10×6
→
Same-bar decision vs a one-session lag: SPY, average session gain, 2016-2025
table 10×5
→
The hindsight ceiling: SPY buy and hold, the same year without its biggest up days, and perfect one-day foresight
table 10×5
→
See all 2,170 queries →