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Biased Auctioneers

Mathieu AubryRoman KräusslGustavo MansoChristophe Spaenjers

出版	Universitätsbibliothek Johann Christian Senckenberg, 2022
URL	http://books.google.com.hk/books?id=vZ3KzwEACAAJ&hl=&source=gbs_api

註釋

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.