| Autor: |
Aubry, Mathieu, Kräussl, Roman, Manso, Gustavo, Spaenjers, Christophe |
| Jazyk: |
angličtina |
| Rok vydání: |
2019 |
| Předmět: |
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| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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