  Time series momentum strategies are widely applied in the quantitative
financial industry and its academic research has grown rapidly since the work
of Moskowitz, Ooi and Pedersen (2012). However, trading signals are usually
obtained via simple observation of past return measurements. In this article we
study the benefits of incorporating dynamic econometric models to sequentially
learn the time-varying importance of different look-back periods for individual
assets. By the use of a dynamic binary classifier model, the investor is able
to switch between time-varying or constant relations between past momentum and
future returns, dynamically combining or selecting different momentum speeds
during turning points, improving trading signals accuracy and portfolio
performance. Using data from 56 future contracts we show that a mean-variance
investor will be willing to pay a considerable management fee to switch from
the traditional naive time series momentum strategy to the dynamic classifier
approach.
