IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2307.13832.html

Multi-Factor Inception: What to Do with All of These Features?

Author

Listed:
  • Tom Liu
  • Stefan Zohren

Abstract

Cryptocurrency trading represents a nascent field of research, with growing adoption in industry. Aided by its decentralised nature, many metrics describing cryptocurrencies are accessible with a simple Google search and update frequently, usually at least on a daily basis. This presents a promising opportunity for data-driven systematic trading research, where limited historical data can be augmented with additional features, such as hashrate or Google Trends. However, one question naturally arises: how to effectively select and process these features? In this paper, we introduce Multi-Factor Inception Networks (MFIN), an end-to-end framework for systematic trading with multiple assets and factors. MFINs extend Deep Inception Networks (DIN) to operate in a multi-factor context. Similar to DINs, MFIN models automatically learn features from returns data and output position sizes that optimise portfolio Sharpe ratio. Compared to a range of rule-based momentum and reversion strategies, MFINs learn an uncorrelated, higher-Sharpe strategy that is not captured by traditional, hand-crafted factors. In particular, MFIN models continue to achieve consistent returns over the most recent years (2022-2023), where traditional strategies and the wider cryptocurrency market have underperformed.

Suggested Citation

  • Tom Liu & Stefan Zohren, 2023. "Multi-Factor Inception: What to Do with All of These Features?," Papers 2307.13832, arXiv.org.
    • Handle: RePEc:arx:papers:2307.13832
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2307.13832
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chao Zhang & Zihao Zhang & Mihai Cucuringu & Stefan Zohren, 2021. "A Universal End-to-End Approach to Portfolio Optimization via Deep Learning," Papers 2111.09170, arXiv.org.
    2. Vidal-Tomás, David, 2022. "Which cryptocurrency data sources should scholars use?," International Review of Financial Analysis, Elsevier, vol. 81(C).
    3. Li, Yi & Urquhart, Andrew & Wang, Pengfei & Zhang, Wei, 2021. "MAX momentum in cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 77(C).
    4. Tom Liu & Stephen Roberts & Stefan Zohren, 2023. "Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies," Papers 2307.05522, arXiv.org.
    5. Yukun Liu & Aleh Tsyvinski & Xi Wu, 2022. "Common Risk Factors in Cryptocurrency," Journal of Finance, American Finance Association, vol. 77(2), pages 1133-1177, April.
    6. Guglielmo Maria Caporale & Alex Plastun, 2020. "Momentum effects in the cryptocurrency market after one-day abnormal returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 34(3), pages 251-266, September.
    7. Yukun Liu & Aleh Tsyvinski, 2021. "Risks and Returns of Cryptocurrency," The Review of Financial Studies, Society for Financial Studies, vol. 34(6), pages 2689-2727.
    8. Lim, Bryan & Arık, Sercan Ö. & Loeff, Nicolas & Pfister, Tomas, 2021. "Temporal Fusion Transformers for interpretable multi-horizon time series forecasting," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1748-1764.
    9. Moskowitz, Tobias J. & Ooi, Yao Hua & Pedersen, Lasse Heje, 2012. "Time series momentum," Journal of Financial Economics, Elsevier, vol. 104(2), pages 228-250.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yichi Zhang & Mihai Cucuringu & Alexander Y. Shestopaloff & Stefan Zohren, 2023. "Dynamic Time Warping for Lead-Lag Relationships in Lagged Multi-Factor Models," Papers 2309.08800, arXiv.org.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dunbar, Kwamie & Owusu-Amoako, Johnson, 2023. "Predictability of crypto returns: The impact of trading behavior," Journal of Behavioral and Experimental Finance, Elsevier, vol. 39(C).
    2. Fieberg, Christian & Günther, Steffen & Poddig, Thorsten & Zaremba, Adam, 2024. "Non-standard errors in the cryptocurrency world," International Review of Financial Analysis, Elsevier, vol. 92(C).
    3. Mercik, Aleksander & Będowska-Sójka, Barbara & Karim, Sitara & Zaremba, Adam, 2025. "Cross-sectional interactions in cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 97(C).
    4. Proelss, Juliane & Schweizer, Denis & Buchwalter, Bastien, 2025. "Do risk preferences drive momentum in cryptocurrencies?," Finance Research Letters, Elsevier, vol. 73(C).
    5. Hoang, Lai & Vo, Duc Hong, 2024. "Google search and cross-section of cryptocurrency returns and trading activities," Journal of Behavioral and Experimental Finance, Elsevier, vol. 44(C).
    6. Zhao, Xiaojuan & Wang, Ye & Liu, Weiyi, 2024. "Someone like you: Lottery-like preference and the cross-section of expected returns in the cryptocurrency market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    7. Bennett, Donyetta & Mekelburg, Erik & Williams, T.H., 2023. "BeFi meets DeFi: A behavioral finance approach to decentralized finance asset pricing," Research in International Business and Finance, Elsevier, vol. 65(C).
    8. Almeida, José & Gonçalves, Tiago Cruz, 2023. "A systematic literature review of investor behavior in the cryptocurrency markets," Journal of Behavioral and Experimental Finance, Elsevier, vol. 37(C).
    9. Tom Liu & Stephen Roberts & Stefan Zohren, 2023. "Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies," Papers 2307.05522, arXiv.org.
    10. Lan, Tian & Frömmel, Michael, 2025. "Risk factors in cryptocurrency pricing," International Review of Financial Analysis, Elsevier, vol. 105(C).
    11. Fieberg, Christian & Liedtke, Gerrit & Zaremba, Adam, 2024. "Cryptocurrency anomalies and economic constraints," International Review of Financial Analysis, Elsevier, vol. 94(C).
    12. Abd Rabbo, Fatima & Disli, Mustafa, 2025. "Style investing and return comovement in the cryptocurrency market," Research in International Business and Finance, Elsevier, vol. 77(PB).
    13. Bui, Mai & Pham, Huy & Nguyen Thanh, Binh & Tiwari, Aviral Kumar, 2024. "Revisiting the determinants of cryptocurrency excess return: Does scarcity matter?," International Review of Economics & Finance, Elsevier, vol. 96(PC).
    14. Li, Yi & Zhang, Wei & Urquhart, Andrew & Wang, Pengfei, 2022. "The role of media coverage in the bubble formation: Evidence from the Bitcoin market," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 80(C).
    15. Cakici, Nusret & Shahzad, Syed Jawad Hussain & Będowska-Sójka, Barbara & Zaremba, Adam, 2024. "Machine learning and the cross-section of cryptocurrency returns," International Review of Financial Analysis, Elsevier, vol. 94(C).
    16. Federico P. Cortese & Petter N. Kolm & Erik Lindström, 2023. "What drives cryptocurrency returns? A sparse statistical jump model approach," Digital Finance, Springer, vol. 5(3), pages 483-518, December.
    17. Long, Huaigang & Demir, Ender & Będowska-Sójka, Barbara & Zaremba, Adam & Shahzad, Syed Jawad Hussain, 2022. "Is geopolitical risk priced in the cross-section of cryptocurrency returns?," Finance Research Letters, Elsevier, vol. 49(C).
    18. Leong, Minhao & Kwok, Simon, 2023. "The pricing of jump and diffusive risks in the cross-section of cryptocurrency returns," Journal of Empirical Finance, Elsevier, vol. 74(C).
    19. Hadhri, Sinda & Younus, Mehak & Naeem, Muhammad Abubakr & Yarovaya, Larisa, 2025. "Listening to the Market: Music sentiment and cryptocurrency returns," Journal of International Money and Finance, Elsevier, vol. 157(C).
    20. Yeguang Chi & Wenyan Hao & Jiangdong Hu & Zhenkai Ran, 2023. "An empirical investigation on risk factors in cryptocurrency futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(8), pages 1161-1180, August.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:2307.13832. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.