The Journal of Finance publishes leading research across all the major fields of finance. It is one of the most widely cited journals in academic finance, and in all of economics. Each of the six issues per year reaches over 8,000 academics, finance professionals, libraries, and government and financial institutions around the world. The journal is the official publication of The American Finance Association, the premier academic organization devoted to the study and promotion of knowledge about financial economics.
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Biased Auctioneers
Published: 01/18/2023 | DOI: 10.1111/jofi.13203
MATHIEU AUBRY, ROMAN KRÄUSSL, GUSTAVO MANSO, CHRISTOPHE SPAENJERS
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.