Hurst Exponent Screener

The Hurst exponent, named after hydrologist Harold Edwin Hurst, quantifies the long-range dependence in a time series. Originally developed to analyze Nile River flooding patterns, it has become a key tool in quantitative finance for distinguishing between trending, random, and mean-reverting price behavior.

Method: H = slope of log(R/S) vs log(n) where R/S is the rescaled range statistic computed over non-overlapping windows of size n.

How to Interpret

H > 0.5 (Trending): Price movements tend to continue in the same direction. A rise is more likely followed by another rise. Momentum and trend-following strategies tend to work well on these stocks.

H = 0.5 (Random Walk): No memory in the price series — past moves provide no information about future direction. This is the Efficient Market Hypothesis baseline.

H < 0.5 (Mean-Reverting): Price movements tend to reverse. A rise is more likely followed by a decline. Mean reversion, pairs trading, and contrarian strategies tend to work well here.

Trading Applications

Use the Hurst exponent to match your trading strategy to the stock's behavior. Filter for H > 0.55 to find momentum candidates, or H < 0.45 for mean-reversion plays. Compare across timeframes — a stock trending on the 1-year horizon but mean-reverting on 3 months suggests a regime change may be underway.

Limitations

The Hurst exponent is estimated from historical data and can change as market regimes shift. R/S analysis requires sufficient data — our screener uses 3-month to 5-year windows with minimum data point requirements. Short-period estimates (3M) are noisier than long-period ones (5Y). The composite score weights longer periods more heavily to account for this. Values near 0.5 may not be statistically distinguishable from a random walk.