Word Embedding-Based Hedonic Models for Valuing Profile Picture NFTs: A Case Study of Bored Ape Yacht Club (81265)
Session Chair: Geun Cheol Lee
Saturday, 25 May 2024 18:00
Session: Session 5
Room: Room 605
Presentation Type: Oral Presentation
This paper proposes a novel method for valuing non-fungible tokens (NFTs) which are unique digital assets that can represent various forms of art, gaming, music, and other creative works. Among them, profile picture (PFP) NFTs are digital collectibles that can be used as avatars on social media platforms. This paper proposes a method for valuing PFP NFTs based on their textual descriptions and features. The method uses word embedding techniques, such as Word2Vec and TF-IDF, to extract numerical vectors from the words that describe the PFP NFTs, and then applies hedonic regression models to estimate the impact of these vectors on the NFT prices. The paper focuses on one of the most popular and valuable PFP NFT collections, the Bored Ape Yacht Club (BAYC), and compares the proposed method with the existing method that uses one-hot encoding method for representing categorical variables. The paper finds that the proposed method has better explanatory power and performance than the existing method. It is expected that the paper will contribute to the literature on NFT valuation and provide insights for NFT collectors and creators.
Authors:
Geun-Cheol Lee, Konkuk University, South Korea
Hoon-Young Koo, Chungnam National University, South Korea
Heejung Lee, Hanyang University, South Korea
About the Presenter(s)
Professor Geun-Cheol Lee is a University Professor/Principal Lecturer at Konkuk University in South Korea
See this presentation on the full schedule – Saturday Schedule
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