Arbeitspapier
Machine learning, human experts, and the valuation of real assets
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.
- Sprache
-
Englisch
- Erschienen in
-
Series: CFS Working Paper Series ; No. 635
- Klassifikation
-
Wirtschaft
Econometric Modeling: General
Auctions
Asset Pricing; Trading Volume; Bond Interest Rates
Cultural Economics: Economics of the Arts and Literature
- Thema
-
asset valuation
auctions
experts
big data
machine learning
computer vision
art
- Ereignis
-
Geistige Schöpfung
- (wer)
-
Aubry, Mathieu
Kräussl, Roman
Manso, Gustavo
Spaenjers, Christophe
- Ereignis
-
Veröffentlichung
- (wer)
-
Goethe University Frankfurt, Center for Financial Studies (CFS)
- (wo)
-
Frankfurt a. M.
- (wann)
-
2019
- Handle
- URN
-
urn:nbn:de:hebis:30:3-515969
- Letzte Aktualisierung
-
10.03.2025, 11:43 MEZ
Datenpartner
Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Arbeitspapier
Beteiligte
- Aubry, Mathieu
- Kräussl, Roman
- Manso, Gustavo
- Spaenjers, Christophe
- Goethe University Frankfurt, Center for Financial Studies (CFS)
Entstanden
- 2019
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