Biased auctioneers

Mathieu Aubry, Roman Kräussl, Gustavo Manso and Christophe Spaenjers

No 692, CFS Working Paper Series from Center for Financial Studies (CFS)

Abstract: 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.

Keywords: art; auctions; experts; asset valuation; biases; machine learning; computer vision (search for similar items in EconPapers)
JEL-codes: C50 D44 G12 Z11 (search for similar items in EconPapers)
Date: 2023
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cul and nep-des
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Journal Article: Biased Auctioneers (2023)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cfswop:692

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