Non-fungible token transactions: data and challenges

TL;DR

This paper examines the unique data challenges in analyzing NFT transaction histories, highlighting issues like price differentiation, wash trades, and volatility, which impact investment decisions and future research opportunities.

Contribution

It identifies and illustrates key data challenges in NFT transaction analysis, providing a foundation for future research in this emerging asset class.

Findings

NFT transaction data exhibit price differentiation by traits

Presence of lateral swaps and wash trades complicates analysis

NFT markets show severe volatility affecting data interpretation

Abstract

Non-fungible tokens (NFT) have recently emerged as a novel blockchain hosted financial asset class that has attracted major transaction volumes. Investment decisions rely on data and adequate preprocessing and application of analytics to them. Both owing to the non-fungible nature of the tokens and to a blockchain being the primary data source, NFT transaction data pose several challenges not commonly encountered in traditional financial data. Using data that consist of the transaction history of eight highly valued NFT collections, a selection of such challenges is illustrated. These are: price differentiation by token traits, the possible existence of lateral swaps and wash trades in the transaction history and finally, severe volatility. While this paper merely scratches the surface of how data analytics can be applied in this context, the data and challenges laid out here may present opportunities for future research on the topic.