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Heterogeneous rarity patterns drive price dynamics in NFT collections

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Listed:
  • Amin Mekacher
  • Alberto Bracci
  • Matthieu Nadini
  • Mauro Martino
  • Laura Alessandretti
  • Luca Maria Aiello
  • Andrea Baronchelli

Abstract

We quantify Non Fungible Token (NFT) rarity and investigate how it impacts market behaviour by analysing a dataset of 3.7M transactions collected between January 2018 and June 2022, involving 1.4M NFTs distributed across 410 collections. First, we consider the rarity of an NFT based on the set of human-readable attributes it possesses and show that most collections present heterogeneous rarity patterns, with few rare NFTs and a large number of more common ones. Then, we analyze market performance and show that, on average, rarer NFTs: (i) sell for higher prices, (ii) are traded less frequently, (iii) guarantee higher returns on investment (ROIs), and (iv) are less risky, i.e., less prone to yield negative returns. We anticipate that these findings will be of interest to researchers as well as NFT creators, collectors, and traders.

Suggested Citation

  • Amin Mekacher & Alberto Bracci & Matthieu Nadini & Mauro Martino & Laura Alessandretti & Luca Maria Aiello & Andrea Baronchelli, 2022. "Heterogeneous rarity patterns drive price dynamics in NFT collections," Papers 2204.10243, arXiv.org, revised Aug 2022.
    • Handle: RePEc:arx:papers:2204.10243
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    References listed on IDEAS

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    1. Koford, Kenneth & Tschoegl, Adrian E., 1998. "The market value of rarity," Journal of Economic Behavior & Organization, Elsevier, vol. 34(3), pages 445-457, March.
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    Cited by:

    1. Niklas Konstantin Klein & Fritz Lattermann & Dirk Schiereck, 2023. "Investment in non-fungible tokens (NFTs): the return of Ethereum secondary market NFT sales," Journal of Asset Management, Palgrave Macmillan, vol. 24(4), pages 241-254, July.
    2. Fridgen, Gilbert & Kräussl, Roman & Papageorgiou, Orestis & Tugnetti, Alessandro, 2023. "The fundamental value of art NFTs," CFS Working Paper Series 709, Center for Financial Studies (CFS).
    3. Mingxuan He, 2023. "Deep Learning for Dynamic NFT Valuation," Papers 2312.05346, arXiv.org.

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