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Biased auctioneers

Author

Listed:
  • Aubry, Mathieu
  • Kräussl, Roman
  • Manso, Gustavo
  • Spaenjers, Christophe

Abstract

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.

Suggested Citation

  • Aubry, Mathieu & Kräussl, Roman & Manso, Gustavo & Spaenjers, Christophe, 2023. "Biased auctioneers," CFS Working Paper Series 692, Center for Financial Studies (CFS).
  • Handle: RePEc:zbw:cfswop:692
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    Other versions of this item:

    • Mathieu Aubry & Roman Kräussl & Gustavo Manso & Christophe Spaenjers, 2023. "Biased Auctioneers," Journal of Finance, American Finance Association, vol. 78(2), pages 795-833, April.

    References listed on IDEAS

    as
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    More about this item

    Keywords

    art; auctions; experts; asset valuation; biases; machine learning; computer vision;
    All these keywords.

    JEL classification:

    • C50 - Mathematical and Quantitative Methods - - Econometric Modeling - - - General
    • D44 - Microeconomics - - Market Structure, Pricing, and Design - - - Auctions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • Z11 - Other Special Topics - - Cultural Economics - - - Economics of the Arts and Literature

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