Trend-Following Strategies via Dynamic Momentum Learning

TL;DR

This paper introduces a dynamic econometric model for time series momentum strategies, enabling adaptive look-back period importance and improving trading signals and portfolio performance in financial markets.

Contribution

It proposes a novel dynamic binary classifier model that adaptively switches between momentum relations, enhancing traditional time series momentum strategies.

Findings

Dynamic model improves trading signal accuracy

Investors willing to pay higher fees for dynamic approach

Enhanced portfolio performance demonstrated on futures data

Abstract

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.