Network Momentum across Asset Classes
Study on network momentum as a trading signal across asset classes using graph learning and pricing data.
What it examines
This paper explores network momentum, a novel trading signal derived from momentum spillover across multiple asset classes using a graph learning model. It aims to construct a multi-asset investment strategy based on these signals, demonstrating its effectiveness through robust empirical analysis.
What it concludes
The research demonstrates the potential of network momentum in multi-asset trading strategies, emphasizing the importance of inter-class connections. Future research could explore other machine learning models and turnover regularization. Potential applications include enhanced portfolio construction and risk management in quantitative finance.
Evidence objects
The research demonstrates the potential of network momentum in multi-asset trading strategies, emphasizing the importance of inter-class connections. Future research could explore other machine learning models and turnover regularization. Potential applications include enhanced portfolio construction and risk management in quantitative finance.
key_findings bullet 1 · key_findings · validation V0
Raw abstract and provenance
Abstract: 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 s… ▽ More 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. △ Less
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