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Do connections pay off in the bitcoin market?

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  • Tsang, Kwok Ping
  • Yang, Zichao

Abstract

This paper identifies the bitcoin investor network and studies the relationship between connections and returns. Using transaction data recorded in the bitcoin blockchain from 2015 to 2020, we reach three conclusions. First, connectedness is not strongly correlated with higher returns in the first four years. However, the correlation becomes strong and significant in 2019 and 2020. Second, returns also differ among those connected addresses. By dividing the connected addresses into ten decile groups based on their centrality, we find that the top 20% most-connected addresses earn higher returns than their peers during most of our sample period. Third, eigenvector centrality is more related to higher returns than degree centrality for the top 20% most-connected addresses, implying that the quality of connections may matter more than quantity among those highly connected addresses.

Suggested Citation

  • Tsang, Kwok Ping & Yang, Zichao, 2022. "Do connections pay off in the bitcoin market?," Journal of Empirical Finance, Elsevier, vol. 67(C), pages 1-18.
  • Handle: RePEc:eee:empfin:v:67:y:2022:i:c:p:1-18
    DOI: 10.1016/j.jempfin.2022.02.001
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    References listed on IDEAS

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    1. Weili Chen & Jun Wu & Zibin Zheng & Chuan Chen & Yuren Zhou, 2019. "Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network," Papers 1902.01941, arXiv.org.
    2. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    3. Han N. Ozsoylev & Johan Walden & M. Deniz Yavuz & Recep Bildik, 2014. "Investor Networks in the Stock Market," The Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1323-1366.
    4. A. Colin Cameron & Douglas L. Miller, 2015. "A Practitioner’s Guide to Cluster-Robust Inference," Journal of Human Resources, University of Wisconsin Press, vol. 50(2), pages 317-372.
    5. Gandal, Neil & Hamrick, JT & Moore, Tyler & Oberman, Tali, 2018. "Price manipulation in the Bitcoin ecosystem," Journal of Monetary Economics, Elsevier, vol. 95(C), pages 86-96.
    6. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    7. Li, Xindan & Geng, Ziyang & Subrahmanyam, Avanidhar & Yu, Honghai, 2017. "Do wealthy investors have an informational advantage? Evidence based on account classifications of individual investors," Journal of Empirical Finance, Elsevier, vol. 44(C), pages 1-18.
    8. Mitchell A. Petersen, 2009. "Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches," The Review of Financial Studies, Society for Financial Studies, vol. 22(1), pages 435-480, January.
    9. Johan Walden, 2019. "Trading, Profits, and Volatility in a Dynamic Information Network Model," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 86(5), pages 2248-2283.
    10. Rossi, Alberto G. & Blake, David & Timmermann, Allan & Tonks, Ian & Wermers, Russ, 2018. "Network centrality and delegated investment performance," Journal of Financial Economics, Elsevier, vol. 128(1), pages 183-206.
    11. Hung, Jui-Cheng & Liu, Hung-Chun & Yang, J. Jimmy, 2021. "Trading activity and price discovery in Bitcoin futures markets," Journal of Empirical Finance, Elsevier, vol. 62(C), pages 107-120.
    12. Brad M. Barber & Yi-Tsung Lee & Yu-Jane Liu & Terrance Odean, 2009. "Just How Much Do Individual Investors Lose by Trading?," The Review of Financial Studies, Society for Financial Studies, vol. 22(2), pages 609-632, February.
    13. Ahern, Kenneth R., 2017. "Information networks: Evidence from illegal insider trading tips," Journal of Financial Economics, Elsevier, vol. 125(1), pages 26-47.
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    Cited by:

    1. Rudkin, Simon & Rudkin, Wanling & Dłotko, Paweł, 2023. "On the topology of cryptocurrency markets," International Review of Financial Analysis, Elsevier, vol. 89(C).
    2. Maria Chiara Pocelli & Manuel L. Esquível & Nadezhda P. Krasii, 2023. "Spectral Analysis for Comparing Bitcoin to Currencies and Assets," Mathematics, MDPI, vol. 11(8), pages 1-21, April.

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    More about this item

    Keywords

    Bitcoin; Networks; Centrality; Asset returns;
    All these keywords.

    JEL classification:

    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • L14 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Transactional Relationships; Contracts and Reputation

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