Lag-one autocorrelation of daily returns: twelve household names, July 2021 to June 2026
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 2026-07-31, from What Is the Efficient Market Hypothesis?.
| ticker | trading_days | lag1_autocorrelation | abs_autocorrelation |
|---|---|---|---|
| WMT | 1252 | 0.057 | 0.057 |
| NVDA | 1251 | -0.036 | 0.036 |
| PG | 1253 | -0.035 | 0.035 |
| JPM | 1253 | 0.02 | 0.02 |
| XOM | 1253 | 0.018 | 0.018 |
| AAPL | 1253 | 0.017 | 0.017 |
| TSLA | 1252 | -0.015 | 0.015 |
| SPY | 1253 | -0.013 | 0.013 |
| HD | 1253 | -0.011 | 0.011 |
| JNJ | 1253 | -0.008 | 0.008 |
| MSFT | 1253 | 0.008 | 0.008 |
| KO | 1253 | -0.005 | 0.005 |
- Rows × columns
- 12 × 4
- 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 |
|---|---|---|---|
ticker |
text | 12 distinct values (AAPL, HD, JNJ…) | |
trading_days |
number | 1,251 to 1,253 | |
lag1_autocorrelation |
number | -0.036 to 0.057 | |
abs_autocorrelation |
number | 0.005 to 0.057 |
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 daily AS (
SELECT ticker,
toDate(toTimeZone(window_start, 'America/New_York')) AS dt,
argMax(toFloat64(close), window_start) AS close_px
FROM global_markets.delayed_stocks_minute_aggs
WHERE ticker IN ('SPY','AAPL','MSFT','KO','JNJ','XOM','JPM','WMT','NVDA','TSLA','PG','HD')
AND toDate(toTimeZone(window_start, 'America/New_York')) >= toDate('2021-07-01')
AND toDate(toTimeZone(window_start, 'America/New_York')) <= toDate('2026-06-30')
AND (toHour(toTimeZone(window_start, 'America/New_York')) * 60
+ toMinute(toTimeZone(window_start, 'America/New_York'))) BETWEEN 570 AND 959
GROUP BY ticker, dt
),
returns AS (
SELECT ticker, dt,
close_px / lagInFrame(close_px) OVER (PARTITION BY ticker ORDER BY dt) - 1 AS ret
FROM daily
),
paired AS (
SELECT ticker, ret,
lagInFrame(ret) OVER (PARTITION BY ticker ORDER BY dt) AS prev_ret
FROM returns
WHERE ret IS NOT NULL AND ret > -0.5 AND ret < 0.5
)
SELECT ticker,
count() AS trading_days,
round(corr(ret, prev_ret), 3) AS lag1_autocorrelation,
round(abs(corr(ret, prev_ret)), 3) AS abs_autocorrelation
FROM paired
WHERE prev_ret IS NOT NULL
GROUP BY ticker
ORDER BY abs_autocorrelation DESC
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 analysisWhat Is the Efficient Market Hypothesis?
What followed each kind of session: next-day outcome by the prior day's move, same twelve names
ranking 5×4
→
The index against the names inside it: calendar years 2021 to 2025, a 34-name large-cap basket
table 5×5
→
Annualized volatility vs total return, 25 large caps, calmest to wildest (~2 years)
ranking 25×3
→
When headlines actually land: article counts by ET clock hour, July 2026
ranking 24×4
→
KO: total cash dividends per share by year
ranking 22×2
→
Month-end closes that set a new high for the window, by year (SPY)
ranking 21×4
→
See all 2,170 queries →