IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2203.12587.html
   My bibliography  Save this paper

Bubble Prediction of Non-Fungible Tokens (NFTs): An Empirical Investigation

Author

Listed:
  • Kensuke Ito
  • Kyohei Shibano
  • Gento Mogi

Abstract

Our study empirically predicts the bubble of non-fungible tokens (NFTs): transferable and unique digital assets on public blockchains. This topic is important because, despite their strong market growth in 2021, NFTs on a project basis have not been investigated in terms of bubble prediction. Specifically, we applied the logarithmic periodic power law (LPPL) model to time-series price data associated with four major NFT projects. The results indicate that, as of December 20, 2021, (i) NFTs, in general, are in a small bubble (a price decline is predicted), (ii) the Decentraland project is in a medium bubble (a price decline is predicted), and (iii) the Ethereum Name Service and ArtBlocks projects are in a small negative bubble (a price increase is predicted). A future work will involve a prediction refinement considering the heterogeneity of NFTs, comparison with other methods, and the use of more enriched data.

Suggested Citation

  • Kensuke Ito & Kyohei Shibano & Gento Mogi, 2022. "Bubble Prediction of Non-Fungible Tokens (NFTs): An Empirical Investigation," Papers 2203.12587, arXiv.org, revised Jun 2022.
    • Handle: RePEc:arx:papers:2203.12587
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2203.12587
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Didier SORNETTE & Guilherme DEMOS & Zhang QUN & Peter CAUWELS & Vladimir FILIMONOV & Qunzhi ZHANG, 2015. "Real-Time Prediction and Post-Mortem Analysis of the Shanghai 2015 Stock Market Bubble and Crash," Swiss Finance Institute Research Paper Series 15-32, Swiss Finance Institute.
    2. repec:men:wpaper:58_2015 is not listed on IDEAS
    3. D. Sornette & A. Johansen, 2001. "Significance of log-periodic precursors to financial crashes," Papers cond-mat/0106520, arXiv.org.
    4. Vladimir Filimonov & Didier Sornette, 2011. "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Papers 1108.0099, arXiv.org, revised Jun 2013.
    5. Kyriazis, Nikolaos & Papadamou, Stephanos & Corbet, Shaen, 2020. "A systematic review of the bubble dynamics of cryptocurrency prices," Research in International Business and Finance, Elsevier, vol. 54(C).
    6. D. Sornette & A. Johansen, 2001. "Significance of log-periodic precursors to financial crashes," Quantitative Finance, Taylor & Francis Journals, vol. 1(4), pages 452-471.
    7. Vytautas Karalevicius & Niels Degrande & Jochen De Weerdt, 2018. "Using sentiment analysis to predict interday Bitcoin price movements," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 19(1), pages 56-75, December.
    8. Jaroslav Bukovina & Matus Marticek, 2016. "Sentiment and Bitcoin Volatility," MENDELU Working Papers in Business and Economics 2016-58, Mendel University in Brno, Faculty of Business and Economics.
    9. Cathy Yi-Hsuan Chen & Christian M. Hafner, 2019. "Sentiment-Induced Bubbles in the Cryptocurrency Market," JRFM, MDPI, vol. 12(2), pages 1-12, April.
    10. Anders Johansen & Didier Sornette, 2010. "Shocks, Crashes and Bubbles in Financial Markets," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 53(2), pages 201-253.
    11. Corbet, Shaen & Lucey, Brian & Yarovaya, Larisa, 2018. "Datestamping the Bitcoin and Ethereum bubbles," Finance Research Letters, Elsevier, vol. 26(C), pages 81-88.
    12. Anders Johansen & Olivier Ledoit & Didier Sornette, 2000. "Crashes As Critical Points," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(02), pages 219-255.
    13. Adrian (Wai-Kong) Cheung & Eduardo Roca & Jen-Je Su, 2015. "Crypto-currency bubbles: an application of the Phillips-Shi-Yu (2013) methodology on Mt. Gox bitcoin prices," Applied Economics, Taylor & Francis Journals, vol. 47(23), pages 2348-2358, May.
    14. Wanfeng Yan & Ryan Woodard & Didier Sornette, 2010. "Diagnosis and Prediction of Tipping Points in Financial Markets: Crashes and Rebounds," Papers 1001.0265, arXiv.org, revised Feb 2010.
    15. Matthieu Nadini & Laura Alessandretti & Flavio Di Giacinto & Mauro Martino & Luca Maria Aiello & Andrea Baronchelli, 2021. "Mapping the NFT revolution: market trends, trade networks and visual features," Papers 2106.00647, arXiv.org, revised Sep 2021.
    16. Vladimir Filimonov & Didier Sornette, "undated". "A Stable and Robust Calibration Scheme of the Log-Periodic Power Law Model," Working Papers ETH-RC-11-002, ETH Zurich, Chair of Systems Design.
    17. Filimonov, V. & Sornette, D., 2013. "A stable and robust calibration scheme of the log-periodic power law model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(17), pages 3698-3707.
    18. Xiong, Jinwu & Liu, Qing & Zhao, Lei, 2020. "A new method to verify Bitcoin bubbles: Based on the production cost," The North American Journal of Economics and Finance, Elsevier, vol. 51(C).
    19. Shu, Min & Zhu, Wei, 2020. "Real-time prediction of Bitcoin bubble crashes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 548(C).
    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. Ludovic Tangpi & Shichun Wang, 2022. "Optimal Bubble Riding: A Mean Field Game with Varying Entry Times," Papers 2209.04001, arXiv.org, revised Jan 2024.
    2. Urom, C. & Ndubuisi, Gideon & Guesmi, K., 2022. "Quantile return and volatility connectedness among Non-Fungible Tokens (NFTs) and (un)conventional asset," MERIT Working Papers 2022-017, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT).

    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. Yao, Can-Zhong & Li, Hong-Yu, 2021. "A study on the bursting point of Bitcoin based on the BSADF and LPPLS methods," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    2. Song, Ruiqiang & Shu, Min & Zhu, Wei, 2022. "The 2020 global stock market crash: Endogenous or exogenous?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 585(C).
    3. Ruiqiang Song & Min Shu & Wei Zhu, 2021. "The 2020 Global Stock Market Crash: Endogenous or Exogenous?," Papers 2101.00327, arXiv.org.
    4. Shu, Min & Zhu, Wei, 2020. "Detection of Chinese stock market bubbles with LPPLS confidence indicator," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    5. Shu, Min & Song, Ruiqiang & Zhu, Wei, 2021. "The ‘COVID’ crash of the 2020 U.S. Stock market," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
    6. Zhang, Qunzhi & Sornette, Didier & Balcilar, Mehmet & Gupta, Rangan & Ozdemir, Zeynel Abidin & Yetkiner, Hakan, 2016. "LPPLS bubble indicators over two centuries of the S&P 500 index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 458(C), pages 126-139.
    7. Sornette, Didier & Woodard, Ryan & Yan, Wanfeng & Zhou, Wei-Xing, 2013. "Clarifications to questions and criticisms on the Johansen–Ledoit–Sornette financial bubble model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(19), pages 4417-4428.
    8. Min Shu & Ruiqiang Song & Wei Zhu, 2021. "The 'COVID' Crash of the 2020 U.S. Stock Market," Papers 2101.03625, arXiv.org.
    9. Riza Demirer & Guilherme Demos & Rangan Gupta & Didier Sornette, 2019. "On the predictability of stock market bubbles: evidence from LPPLS confidence multi-scale indicators," Quantitative Finance, Taylor & Francis Journals, vol. 19(5), pages 843-858, May.
    10. Bikramaditya Ghosh & Spyros Papathanasiou & Georgios Pergeris, 2022. "Did cryptocurrencies exhibit log‐periodic power law signature during the second wave of COVID‐19?," Economic Notes, Banca Monte dei Paschi di Siena SpA, vol. 51(3), November.
    11. Rebecca Westphal & Didier Sornette, 2019. "Market Impact and Performance of Arbitrageurs of Financial Bubbles in An Agent-Based Model," Swiss Finance Institute Research Paper Series 19-29, Swiss Finance Institute.
    12. Bikramaditya Ghosh & Spyros Papathanasiou & Nikita Ramchandani & Dimitrios Kenourgios, 2021. "Diagnosis and Prediction of IIGPS’ Countries Bubble Crashes during BREXIT," Mathematics, MDPI, vol. 9(9), pages 1-14, April.
    13. Demos, G. & Sornette, D., 2019. "Comparing nested data sets and objectively determining financial bubbles’ inceptions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 524(C), pages 661-675.
    14. Kristoffer Pons Bertelsen, 2019. "Comparing Tests for Identification of Bubbles," CREATES Research Papers 2019-16, Department of Economics and Business Economics, Aarhus University.
    15. Min Shu & Ruiqiang Song & Wei Zhu, 2021. "The 2021 Bitcoin Bubbles and Crashes—Detection and Classification," Stats, MDPI, vol. 4(4), pages 1-21, November.
    16. Bikramaditya Ghosh & Spyros Papathanasiou & Vandita Dar & Dimitrios Kenourgios, 2022. "Deconstruction of the Green Bubble during COVID-19 International Evidence," Sustainability, MDPI, vol. 14(6), pages 1-18, March.
    17. Westphal, Rebecca & Sornette, Didier, 2020. "Market impact and performance of arbitrageurs of financial bubbles in an agent-based model," Journal of Economic Behavior & Organization, Elsevier, vol. 171(C), pages 1-23.
    18. Li, Chong, 2017. "Log-periodic view on critical dates of the Chinese stock market bubbles," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 305-311.
    19. Fantazzini, Dean & Nigmatullin, Erik & Sukhanovskaya, Vera & Ivliev, Sergey, 2016. "Everything you always wanted to know about bitcoin modelling but were afraid to ask. I," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 44, pages 5-24.
    20. Ozkan Haykir & Ibrahim Yagli, 2022. "Speculative bubbles and herding in cryptocurrencies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-33, December.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:2203.12587. 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.