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A unit root test for stationarity that augments the Dickey-Fuller equation with lagged differences.
A unit root test for stationarity that augments the Dickey-Fuller equation with lagged differences.
The ADF test evaluates the null hypothesis that a time series has a unit root (is non-stationary) against the alternative of stationarity. It is the workhorse pre-test for ARMA modeling and for confirming mean reversion in trading strategies. Critical values are non-standard and depend on the inclusion of constant and trend terms.
Reject the null (γ < 0, ADF stat below critical value) to claim stationarity.
Lag order selection (p) by AIC/BIC is critical for test power and size.
Low power against near-unit-root alternatives use alongside KPSS for robustness.
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).