DATART: DATA analytics for ART-valuation
The project at a glance
About
The importance that collectibles play in wealth management is becoming increasingly crucial, and this necessitates the development of models which can efficiently predict their price fluctuations. In this research, we will exploit the ways in which technology is changing the art market by applying supervised machine learning methods to artistic products. The objective will be to develop a pricing method that is on one hand more accurate, and on the other that maintains the level of interpretability of the models currently used in the industry. In particular, art pricing is typically concerned with estimating Ordinary Least Squares (OLS) models, leveraging on their interpretability potential. However, it is known how these algorithms have small predictive capacity when compared with more flexible models in sectors where the uncertainty and human bias of valuation experts become stronger. Once we take advantage of the strength and abundance of data existing on the “physical” art framework, we will extend the reasoning to the Non Fungible Token (NFT) market. The goal here would be to understand the “”value-creating factors”” of NFTs, to compare them with those of classical works of art and discover points of synergy and detachment between these two worlds.
Organisation and Partners
Faculty of Law, Economics and Finance (FDEF)
Institute for Advanced Studies (IAS)
Project team
Prof Gilbert FRIDGEN
Full professor in Digital Financial Services / Paypal-FNR PEARL Chair