Mathematics Research Seminar Series: Collection effects on volume, risk, and returns of NFT transactions

Mathematics Research Seminar Series
Collection effects on volume, risk, and returns of NFT transactions

by Emmanuel “EC” Plan
Hanoi School of Business and Management (HSB) - Vietnam National University Hanoi

Date: Tuesday, 06 Feb 2024
Time: 5:00 - 6:00 pm
Venue: (Onsite) Sec A 302
            (Online) https://bit.ly/AdmuMathSeminar 

Abstract:
Non-fungible tokens (NFTs) are emerging financial assets based on blockchains. They often appear as part of a collection, and collection characteristics can have an impact on the trading dynamics of investors. In this talk, I will share how these characteristics affect transactional volume, risk, and return of NFT collections by using a panel data approach. Our results show that older collections, those created by more popular labs, and tokens with additional earning options exhibited larger price fluctuations. Higher returns were seen from NFT projects from popular NFT creators particularly when commercial rights to use the tokens were given. Moreover, we uncovered non-trivial determining and predicting relationships between volume, risk and return. Lastly, we also report a significant positive relationship between return and volatility using our dataset from which a modest amount of wash trades was eliminated; this result is in contrast to a significant negative relationship when using the full dataset or when compared to existing literature.

About the Speaker:
Emmanuel "EC" Plan is a graduate of Ateneo de Manila University (BS Mathematics, 2010). He finished his Masters of Science in National University of Singapore (2013) and has a doctorate in Physics from Universite Cote d'Azur (France, 2017). He has had research positions from the University of Oxford (UK) and Duy Tan University (Vietnam). Starting in 2022, he is a lecturer and researcher at the Hanoi School of Business and Management, Vietnam National University Hanoi. His research interests are in data science and machine learning applied in business and management, non-traditional security, mathematical modelling, among others.