Nonlinear Time Series Momentum

Abstract

We document a persistent nonlinear relationship between price trends and risk-adjusted returns across markets and asset classes that is consistent with asset pricing theory. Nonlinearities in time series momentum are consistent with past returns reflecting information about conditional expected returns, in line with investors using conditioning information to form efficient portfolios. Machine learning techniques are useful in uncovering these relationships and yield economically and statistically significant out-of-sample improvements in time series momentum strategies.

Keywords: Time Series Momentum, Return Forecasts, Nonlinear Regression, Machine Learning

JEL Classification: G17, C53, C58, G11