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Evidence for round number effects in cryptocurrencies prices

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  • Quiroga-Garcia, Raquel
  • Pariente-Martinez, Natalia
  • Arenas-Parra, Mar

Abstract

This paper analyses the relationship between price clustering and trade volume in the Ether, Ripple and Litecoin cryptocurrencies. We examine at which digits price clustering exists and study the behaviour at different price levels and time frames. By using recent data to provide an updated view of price clustering in the cryptocurrency market, we find a remarkable level of price clustering at round prices: 5.29%, 2.84% and 2.97% for Ether, Ripple and Litecoin for every one minute at open prices, respectively. This paper reaffirms the negotiation hypothesis by finding that price clustering appears at prices at which traded volume is higher.

Suggested Citation

  • Quiroga-Garcia, Raquel & Pariente-Martinez, Natalia & Arenas-Parra, Mar, 2022. "Evidence for round number effects in cryptocurrencies prices," Finance Research Letters, Elsevier, vol. 47(PB).
  • Handle: RePEc:eee:finlet:v:47:y:2022:i:pb:s1544612322001179
    DOI: 10.1016/j.frl.2022.102811
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    1. Aitken, Michael & Brown, Philip & Buckland, Christine & Izan, H. Y. & Walter, Terry, 1996. "Price clustering on the Australian Stock Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 4(2-3), pages 297-314, July.
    2. Brown, Alasdair & Yang, Fuyu, 2016. "Limited cognition and clustered asset prices: Evidence from betting markets," Journal of Financial Markets, Elsevier, vol. 29(C), pages 27-46.
    3. Grossman, Sanford J & Miller, Merton H & Cone, Kenneth R & Fischel, Daniel R & Ross, David J, 1997. "Clustering and Competition in Asset Markets," Journal of Law and Economics, University of Chicago Press, vol. 40(1), pages 23-60, April.
    4. Hu, Bill & McInish, Thomas & Miller, Jonathan & Zeng, Li, 2019. "Intraday price behavior of cryptocurrencies," Finance Research Letters, Elsevier, vol. 28(C), pages 337-342.
    5. Robert I. Webb & Jason Mitchell, 2001. "Clustering and psychological barriers: the importance of numbers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(5), pages 395-428, May.
    6. Bariviera, Aurelio F. & Basgall, María José & Hasperué, Waldo & Naiouf, Marcelo, 2017. "Some stylized facts of the Bitcoin market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 82-90.
    7. Balcilar, Mehmet & Bouri, Elie & Gupta, Rangan & Roubaud, David, 2017. "Can volume predict Bitcoin returns and volatility? A quantiles-based approach," Economic Modelling, Elsevier, vol. 64(C), pages 74-81.
    8. David Yermack, 2013. "Is Bitcoin a Real Currency? An economic appraisal," NBER Working Papers 19747, National Bureau of Economic Research, Inc.
    9. Palao, Fernando & Pardo, Angel, 2012. "Assessing price clustering in European Carbon Markets," Applied Energy, Elsevier, vol. 92(C), pages 51-56.
    10. Cheah, Eng-Tuck & Fry, John, 2015. "Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin," Economics Letters, Elsevier, vol. 130(C), pages 32-36.
    11. David L. Ikenberry & James P. Weston, 2008. "Clustering in US Stock Prices after Decimalisation," European Financial Management, European Financial Management Association, vol. 14(1), pages 30-54, January.
    12. Katsiampa, Paraskevi, 2017. "Volatility estimation for Bitcoin: A comparison of GARCH models," Economics Letters, Elsevier, vol. 158(C), pages 3-6.
    13. Brown, Philip & Chua, Angeline & Mitchell, Jason, 2002. "The influence of cultural factors on price clustering: Evidence from Asia-Pacific stock markets," Pacific-Basin Finance Journal, Elsevier, vol. 10(3), pages 307-332, June.
    14. Huimin Chung & Shumei Chiang, 2006. "Price clustering in E‐mini and floor‐traded index futures," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 26(3), pages 269-295, March.
    15. Dwyer, Gerald P., 2015. "The economics of Bitcoin and similar private digital currencies," Journal of Financial Stability, Elsevier, vol. 17(C), pages 81-91.
    16. Dyhrberg, Anne Haubo, 2016. "Hedging capabilities of bitcoin. Is it the virtual gold?," Finance Research Letters, Elsevier, vol. 16(C), pages 139-144.
    17. Bharati, Rakesh & Crain, Susan J. & Kaminski, Vincent, 2012. "Clustering in crude oil prices and the target pricing zone hypothesis," Energy Economics, Elsevier, vol. 34(4), pages 1115-1123.
    18. Dowling, Michael & Cummins, Mark & Lucey, Brian M., 2016. "Psychological barriers in oil futures markets," Energy Economics, Elsevier, vol. 53(C), pages 293-304.
    19. Li, Xin & Li, Shenghong & Xu, Chong, 2020. "Price clustering in Bitcoin market—An extension," Finance Research Letters, Elsevier, vol. 32(C).
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    More about this item

    Keywords

    Cryptocurrencies; Cluster; Volume; Intraday prices;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G15 - Financial Economics - - General Financial Markets - - - International Financial Markets

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