Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/268894 
Erscheinungsjahr: 
2023
Schriftenreihe/Nr.: 
CFS Working Paper Series No. 692
Verlag: 
Goethe University Frankfurt, Center for Financial Studies (CFS), Frankfurt a. M.
Zusammenfassung: 
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.
Schlagwörter: 
art
auctions
experts
asset valuation
biases
machine learning
computer vision
JEL: 
C50
D44
G12
Z11
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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