Picture For Proof(PFPs): Aesthetics, IP and post launch performance

Yingjie Tian, Yuhao Xie, Duo Su and Xiaoxi Zhao

Finance Research Letters, 2023, vol. 55, issue PB

Abstract: This study distinguishes PFPs (Picture For Proof) from other NFT categories, as PFPs are primarily driven by aesthetics and community engagement. This work uses the Backward Sup Augmented Dickey–Fuller (BSADF) test to detect bubbles in the time-series market capitalization data. 13 projects are selected. We utilize t-distributed stochastic neighbor embedding(T-SNE) visualization for style similarity analysis. We also leverage Visual Geometry Group (VGG)-19 as the feature extractor for image similarity within the collections. Our results indicate that PFP collections with intellectual property (IP) rights and low image similarity exhibit the largest financial gain in the long term.

Keywords: Deep learning; Non-fungible tokens; Post launch performance; Aesthetics; Picture For Proof(PFPs) (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:55:y:2023:i:pb:s154461232300346x

DOI: 10.1016/j.frl.2023.103974

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