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Network Momentum across Asset Classes

Author

Listed:
  • Xingyue Pu
  • Stephen Roberts
  • Xiaowen Dong
  • Stefan Zohren
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.

Suggested Citation

  • Xingyue Pu & Stephen Roberts & Xiaowen Dong & Stefan Zohren, 2023. "Network Momentum across Asset Classes," Papers 2308.11294, arXiv.org.
    • Handle: RePEc:arx:papers:2308.11294
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    File URL: http://arxiv.org/pdf/2308.11294
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    References listed on IDEAS

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    1. Pitkäjärvi, Aleksi & Suominen, Matti & Vaittinen, Lauri, 2020. "Cross-asset signals and time series momentum," Journal of Financial Economics, Elsevier, vol. 136(1), pages 63-85.
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    3. Tobias J. Moskowitz & Mark Grinblatt, 1999. "Do Industries Explain Momentum?," Journal of Finance, American Finance Association, vol. 54(4), pages 1249-1290, August.
    4. Wee Ling Tan & Stephen Roberts & Stefan Zohren, 2023. "Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies," Papers 2302.10175, arXiv.org.
    5. Abuelfadl, Moustafa & Yamani, Ehab, 2021. "Currency news and international bond markets," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    6. Gebhardt, William R. & Hvidkjaer, Soeren & Swaminathan, Bhaskaran, 2005. "Stock and bond market interaction: Does momentum spill over?," Journal of Financial Economics, Elsevier, vol. 75(3), pages 651-690, March.
    7. Kieran Wood & Sven Giegerich & Stephen Roberts & Stefan Zohren, 2021. "Trading with the Momentum Transformer: An Intelligent and Interpretable Architecture," Papers 2112.08534, arXiv.org, revised Nov 2022.
    8. Lauren Cohen & Andrea Frazzini, 2008. "Economic Links and Predictable Returns," Journal of Finance, American Finance Association, vol. 63(4), pages 1977-2011, August.
    9. Haesen, Daniel & Houweling, Patrick & van Zundert, Jeroen, 2017. "Momentum spillover from stocks to corporate bonds," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 28-41.
    10. Clifford S. Asness & Tobias J. Moskowitz & Lasse Heje Pedersen, 2013. "Value and Momentum Everywhere," Journal of Finance, American Finance Association, vol. 68(3), pages 929-985, June.
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    Cited by:

    1. Joel Ong & Dorien Herremans, 2024. "DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts," Papers 2406.08742, arXiv.org.
    2. Kieran Wood & Samuel Kessler & Stephen J. Roberts & Stefan Zohren, 2023. "Few-Shot Learning Patterns in Financial Time-Series for Trend-Following Strategies," Papers 2310.10500, arXiv.org, revised Mar 2024.

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