TY  - UNPD
A1  - Aubry, Mathieu
A1  - Kräussl, Roman
A1  - Manso, Gustavo
A1  - Spaenjers, Christophe
T1  - Biased auctioneers
N2  - 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.
T3  - CFS working paper series - No. 692 
KW  - art
KW  - auctions
KW  - experts
KW  - asset valuation
KW  - biases
KW  - machine learning
KW  - computer visionbiases
KW  - computer vision
Y1  - 2022
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/68148
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-681486
UR  - https://ssrn.com/abstract=3347175
VL  - This version: January 6, 2022
PB  - Center for Financial Studies
CY  - Frankfurt, M.
ER  -