Concept
Autocorrelation
Autocorrelation is a Statistics concept. The Library holds 3 implementations, each one a working definition you can pull into Quant.
ACF/PACF
Top Autocorrelation indicators
3 total
What is Autocorrelation?
Autocorrelation is the correlation of a series with a lagged copy of itself. The autocorrelation function (ACF) reports it across lags: the lag-5 value measures how strongly today's observation moves with the one five bars back. The partial autocorrelation function (PACF) isolates each lag's direct effect by stripping out the influence of shorter lags, which is why the ACF/PACF pair is the classic tool for choosing autoregressive and moving-average orders in forecasting models such as ARIMA.
On returns, autocorrelation is a persistence read: significantly positive values mean moves tend to follow through, negative values mean they tend to snap back, and near-zero is what liquid markets usually show. On price, regular peaks in the ACF at evenly spaced lags reveal cyclicality, the idea behind the autocorrelation periodogram. Like any correlation, the estimate is sample-dependent and drifts across regimes.
How traders use it
- As a rolling regime gauge: the sign and size of short-lag return autocorrelation helps classify a market as momentum-friendly or mean-reversion-friendly, alongside tools like the Hurst exponent.
- For model identification: ACF and PACF shapes suggest how many autoregressive and moving-average terms a forecasting model needs before anything is fit.
- For cycle detection: repeating ACF peaks flag a candidate period worth measuring with dedicated cycle tools.
Related concepts · Relationships
Concept family
Statistics
45 concepts mapped · 37 in the Library
Autocorrelation FAQ
What is the difference between ACF and PACF?
The ACF at lag k is the plain correlation between the series and itself k bars back, so it includes effects passed along through intermediate lags. The PACF removes those intermediate effects and isolates the direct lag-k relationship. In model identification, a sharp PACF cutoff suggests an autoregressive order, while a sharp ACF cutoff suggests a moving-average order.
What does negative autocorrelation in returns mean?
At the measured lag, up moves have tended to be followed by down moves and vice versa, which is a mean-reversion signature. It is a tendency in that sample, not a rule: the value drifts across regimes, depends on the lookback, and small readings can be indistinguishable from noise without a significance test.
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