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A measure of long-term memory in a time series, where H = 0.5 indicates a random walk.
A measure of long-term memory in a time series, where H = 0.5 indicates a random walk.
The Hurst exponent characterizes the scaling behavior of the range of cumulative deviations: R/S ~ T^H. H = 0.5 indicates an uncorrelated random walk; H > 0.5 indicates persistent (trending) behavior; H < 0.5 indicates mean-reverting (anti-persistent) behavior. Estimated via rescaled range analysis, DFA, or wavelet methods.
H = 0.5 is the null of weak-form market efficiency.
Equity indices typically show H ≈ 0.5–0.6 over long horizons; trending biases appear.
Estimated Hurst exponents are notoriously biased in finite samples.
Quantitative research is the disciplined process of turning a financial hypothesis into a validated, deployable trading model. It spans data engineering, signal design, backtesting with proper out-of-sample validation, and ongoing production monitoring. The discipline's central problem is overfitting: most strategies that pass backtests fail in production.
Financial machine learning is the application of supervised, unsupervised, and reinforcement learning methods to financial prediction, classification, and decision problems. The defining methodological constraint is that financial data are serially correlated, not independently and identically distributed, which means that the standard machine learning toolkit must be substantially adapted. The dominant practitioner reference is López de Prado (2018).