Biased Auctioneers

• 成果类型: Article
• 署名作者: Aubry, Mathieu; Kraussl, Roman; Manso, Gustavo; Spaenjers, Christophe
• 署名单位: Universite Gustave-Eiffel; Centre National de la Recherche Scientifique (CNRS); ESIEE Paris; Institut Polytechnique de Paris; Ecole des Ponts ParisTech; University of Luxembourg; Stanford University; University of California System; University of California Berkeley; University of Colorado System; University of Colorado Boulder
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13203
• 发表日期: 2023
• 页码: 795-833
• 关键词: ART; returns; prices

摘要

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.