Exposé de Christophe Spaenjers (HEC Paris) dans le cadre du Séminaire Digital Humanities / Artificial Intelligence (DHAI).
Biased Auctioneers (with Mathieu Aubry, Roman Kräussl, and Gustavo Manso)
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
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Christophe Spaenjers est Professeur Associé de finance à HEC Paris. Il a obtenu son doctorat à l'Université de Tilburg. Au cours de son doctorat, il a également étudié à la London Business School, la Columbia Business School et l'International Center for Finance de l'Université Yale.
Ses recherches portent sur les investissements, le comportement des investisseurs, les finances des ménages, les finances d'entreprise et l'histoire de la finance.
Il a publié dans des revues telles que American Economic Review, Review of Financial Studies, Journal of Financial Economics, et Management Science. Christophe Spaenjers enseigne dans les programmes de MBA.
Dernière mise à jour : 28/03/2022