Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/206414 
Erscheinungsjahr: 
2019
Schriftenreihe/Nr.: 
CFS Working Paper Series No. 635
Verlag: 
Goethe University Frankfurt, Center for Financial Studies (CFS), Frankfurt a. M.
Zusammenfassung: 
We study the accuracy and usefulness of automated (i.e., machine-generated) valuations for illiquid and heterogeneous real assets. We assemble a database of 1.1 million paintings auctioned between 2008 and 2015. We use a popular machine-learning technique - neural networks - to develop a pricing algorithm based on both non-visual and visual artwork characteristics. Our out-of-sample valuations predict auction prices dramatically better than valuations based on a standard hedonic pricing model. Moreover, they help explaining price levels and sale probabilities even after conditioning on auctioneers' pre-sale estimates. Machine learning is particularly helpful for assets that are associated with high price uncertainty. It can also correct human experts' systematic biases in expectations formation - and identify ex ante situations in which such biases are likely to arise.
Schlagwörter: 
asset valuation
auctions
experts
big data
machine learning
computer vision
art
JEL: 
C50
D44
G12
Z11
Persistent Identifier der Erstveröffentlichung: 
Dokumentart: 
Working Paper

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