Network Momentum across Asset Classes

ArXiv ID: 2308.11294 “View on arXiv”

Authors: Unknown

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 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.

Keywords: Momentum Spillover, Network Analysis, Graph Learning, Multi-Asset Class, Commodities

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 8.5/10
• Quadrant: Holy Grail
• Why: The paper employs a linear graph learning model, a mathematically advanced concept requiring matrix operations and optimization, but keeps it interpretable; it demonstrates high empirical rigor with a multi-asset backtest over 22 years (2000-2022), robustness analysis, and specific performance metrics like Sharpe ratio and annual returns.

  flowchart TD
    A["Research Goal<br>Investigate Network Momentum<br>across 64 Futures"] --> B["Data Input<br>64 Continuous Futures<br>Commodities, Equities, Bonds, Currencies"]
    B --> C["Methodology<br>Linear Graph Learning Model"]
    C --> D["Computation<br>Construct Momentum<br>Spillover Network"]
    D --> E["Strategy<br>Network Momentum Strategy<br>Volatility Scaling"]
    E --> F["Outcomes<br>Sharpe Ratio: 1.5<br>Annual Return: 22%<br>2000-2022"]