Biased Auctioneers |
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| Authors: | MATHIEU AUBRY ROMAN KRÄUSSL GUSTAVO MANSO CHRISTOPHE SPAENJERS |
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| Institution: | Mathieu Aubry is at LIGM, Ecole des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-Vallée, France. Roman Kräussl is at the University of Luxembourg and at Hoover Institution, Stanford University. Gustavo Manso is at the Haas School of Business, University of California at Berkeley. Christophe Spaenjers is at the Leeds School of Business, University of Colorado Boulder. This paper was written while Spaenjers was at HEC Paris. The authors thank Stefan Nagel (Editor), two anonymous referees, Simona Abis, Will Goetzmann, Katy Graddy, Mara Lederman, Stefano Lovo, Ramana Nanda, Christophe Pérignon, Luc Renneboog, Donghwa Shin, Kelly Shue, David Sraer, Léa Stern, Scott Stern, and conference participants at the 2018 ULB Art Market Workshop, the 2019 HEC Paris Data Day, the 2019 Northeastern University Finance Conference, the 2019 NBER Economics of AI Conference, the 2019 Miami Behavioral Finance Conference, the 2020 GSU-RFS FinTech Conference, and the 2020 WFA for helpful comments. This research is supported by the following grants of the French National Research Agency (ANR): EnHerit (ANR-17-CE23-0008) and “Investissements d'Avenir” (LabEx Ecodec/ANR-11-LABX-0047). Spaenjers has been a regular consultant to Overstone Art Services, an art market advisory firm. The authors have nothing else to disclose. |
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| Abstract: | We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and nonvisual object characteristics. We find that higher automated valuations relative to auction house presale 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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