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Dissecting returns of non-fungible tokens (NFTs): Evidence from CryptoPunks

Author

Listed:
  • Wang, Jying-Nan
  • Lee, Yen-Hsien
  • Liu, Hung-Chun
  • Hsu, Yuan-Teng

Abstract

We use transaction data on CryptoPunks to dissect the factors affecting the returns of non-fungible tokens (NFTs). Our results show that trading volume in the short period before a trader buys (sells) CryptoPunk relates negatively (positively) to the returns on NFTs, suggesting that when market trading volume is at a high level, NFT owners are better off on the sell side, and investors interested in NFTs should avoid joining the herd. Turnover of a token tends to harm its returns. Finally, both traders’ willingness to purchase and trading experience have a positive impact on NFT returns within short-term investment horizons.

Suggested Citation

  • Wang, Jying-Nan & Lee, Yen-Hsien & Liu, Hung-Chun & Hsu, Yuan-Teng, 2023. "Dissecting returns of non-fungible tokens (NFTs): Evidence from CryptoPunks," The North American Journal of Economics and Finance, Elsevier, vol. 65(C).
    • Handle: RePEc:eee:ecofin:v:65:y:2023:i:c:s1062940823000153
    DOI: 10.1016/j.najef.2023.101892
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    References listed on IDEAS

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    More about this item

    Keywords

    NFTs; CryptoPunks; Ethereum; COVID-19; Cryptocurrency;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • 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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