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The correlation of a time series with a lagged version of itself.
The correlation of a time series with a lagged version of itself.
Autocorrelation measures the linear dependence between observations separated by k time steps. In finance, return autocorrelations are typically near zero (weak-form market efficiency), but volatility autocorrelations are strongly positive (volatility clustering). Test statistics include Ljung-Box and Durbin-Watson.
Positive autocorrelation in returns suggests momentum; negative suggests mean reversion.
Squared returns exhibit strong positive autocorrelation the basis of GARCH models.
Ljung-Box Q-statistic tests the joint significance of multiple autocorrelations.
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).
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.