Autoregressive Lag Features

Lag 2 has a stronger negative correlation with current close log return than lag 1. The autoregressive model predicts current log return from previous log returns. A negative lag correlation is interpreted as a mean-reversion signal. The feature set includes lagged log returns from one, two, and three hours earlier.