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The Structure of Cryptocurrency Returns

Author

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  • Shams, Amin

    (Ohio State U)

Abstract

This paper documents a persistent structure in cryptocurrency returns and analyzes a broad set of characteristics that explain this structure. The results show that similarities in size, trading volume, age, consensus mechanism, and token industries drive the structure of cryptocurrency returns. But the highest variation is explained by a "connectivity" measure that proxies for similarity in cryptocurrencies' investor bases using their trading location. Currencies connected to other currencies that perform well generate sizably higher returns than the cross-section both contemporaneously and in the future. I examine three potential channels for these results. First, evidence from new exchange listings and a quasi-natural experiment shows that unobservable characteristics cannot explain the effect of connectivity. Second, decomposition of the order flows suggests that connectivity captures strong exchange-specific commonalities in crypto investors' demand that also spills over to other exchanges. Finally, analysis of social media data suggests that these demand shocks are a first order driver of cryptocurrency returns, largely because they can be perceived as a sign of user adoption.

Suggested Citation

  • Shams, Amin, 2020. "The Structure of Cryptocurrency Returns," Working Paper Series 2020-11, Ohio State University, Charles A. Dice Center for Research in Financial Economics.
    • Handle: RePEc:ecl:ohidic:2020-11
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    Citations

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    Cited by:

    1. Jia, Yuecheng & Wu, Yangru & Yan, Shu & Liu, Yuzheng, 2023. "A seesaw effect in the cryptocurrency market: Understanding the return cross predictability of cryptocurrencies," Journal of Empirical Finance, Elsevier, vol. 74(C).
    2. Lin William Cong & Xi Li & Ke Tang & Yang Yang, 2023. "Crypto Wash Trading," Management Science, INFORMS, vol. 69(11), pages 6427-6454, November.
    3. Ye Li & Simon Mayer & Simon Mayer, 2021. "Money Creation in Decentralized Finance: A Dynamic Model of Stablecoin and Crypto Shadow Banking," CESifo Working Paper Series 9260, CESifo.
    4. Paola Stolfi & Mauro Bernardi & Davide Vergni, 2022. "Robust estimation of time-dependent precision matrix with application to the cryptocurrency market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-25, December.
    5. Raphael Auer & Giulio Cornelli & Sebastian Doerr & Jon Frost & Leonardo Gambacorta, 2022. "Crypto trading and Bitcoin prices: evidence from a new database of retail adoption," BIS Working Papers 1049, Bank for International Settlements.
    6. Lin William Cong & Yizhou Xiao, 2021. "Categories and Functions of Crypto-Tokens," Springer Books, in: Maurizio Pompella & Roman Matousek (ed.), The Palgrave Handbook of FinTech and Blockchain, edition 1, chapter 0, pages 267-284, Springer.
    7. Matteo Benetton & Giovanni Compiani, 2020. "Investors’ Beliefs and Asset Prices: A Structural Model of Cryptocurrency Demand," Working Papers 2020-107, Becker Friedman Institute for Research In Economics.
    8. Borri, Nicola & Shakhnov, Kirill, 2023. "Cryptomarket discounts," Journal of International Money and Finance, Elsevier, vol. 139(C).
    9. Guo, Li & Sang, Bo & Tu, Jun & Wang, Yu, 2024. "Cross-cryptocurrency return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 163(C).

    More about this item

    JEL classification:

    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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