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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?.

as of ranking 12×4read in context →
Lag-one autocorrelation of daily returns: twelve household names, July 2021 to June 2026 — 12 rows by 4 columns, computed from US exchange, SIP and OPRA data.
tickertrading_dayslag1_autocorrelationabs_autocorrelation
WMT12520.0570.057
NVDA1251-0.0360.036
PG1253-0.0350.035
JPM12530.020.02
XOM12530.0180.018
AAPL12530.0170.017
TSLA1252-0.0150.015
SPY1253-0.0130.013
HD1253-0.0110.011
JNJ1253-0.0080.008
MSFT12530.0080.008
KO1253-0.0050.005
Rows × columns
12 × 4
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 Lag-one autocorrelation of daily returns: twelve household names, July 2021 to June 2026, derived from the stored result.
ColumnTypeRangeNotes
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

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