  We investigate the concept of network momentum, a novel trading signal
derived from momentum spillover across assets. Initially observed within the
confines of pairwise economic and fundamental ties, such as the stock-bond
connection of the same company and stocks linked through supply-demand chains,
momentum spillover implies a propagation of momentum risk premium from one
asset to another. The similarity of momentum risk premium, exemplified by
co-movement patterns, has been spotted across multiple asset classes including
commodities, equities, bonds and currencies. However, studying the network
effect of momentum spillover across these classes has been challenging due to a
lack of readily available common characteristics or economic ties beyond the
company level. In this paper, we explore the interconnections of momentum
features across a diverse range of 64 continuous future contracts spanning
these four classes. We utilise a linear and interpretable graph learning model
with minimal assumptions to reveal the intricacies of the momentum spillover
network. By leveraging the learned networks, we construct a network momentum
strategy that exhibits a Sharpe ratio of 1.5 and an annual return of 22%, after
volatility scaling, from 2000 to 2022. This paper pioneers the examination of
momentum spillover across multiple asset classes using only pricing data,
presents a multi-asset investment strategy based on network momentum, and
underscores the effectiveness of this strategy through robust empirical
analysis.

Portfolio Management;Machine Learning;Signal Processing;Trading and Market Microstructure