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Information extraction and artwork pricing

Author

Listed:
  • Jaehyuk Choi
  • Lan Ju
  • Jian Li
  • Zhiyong Tu

Abstract

Traditional art pricing models often lack fine measurements of painting content. This paper proposes a new content measurement: the Shannon information quantity measured by the singular value decomposition (SVD) entropy of the painting image. Using a large sample of artworks' auction records and images, we show that the SVD entropy positively affects the sales price at 1% significance level. Compared to the other commonly adopted content variables, the SVD entropy has advantages in variable significance, sample robustness as well as model fit. Considering the convenient availability of digital painting images and the straightforward calculation algorithm of this measurement, we expect its wide application in future research.

Suggested Citation

  • Jaehyuk Choi & Lan Ju & Jian Li & Zhiyong Tu, 2023. "Information extraction and artwork pricing," Papers 2302.08167, arXiv.org.
    • Handle: RePEc:arx:papers:2302.08167
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    References listed on IDEAS

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    3. Ma, Marshall Xiaoyin & Noussair, Charles N. & Renneboog, Luc, 2022. "Colors, Emotions, and the Auction Value of Paintings," European Economic Review, Elsevier, vol. 142(C).
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    7. repec:hal:pseose:hal-00812682 is not listed on IDEAS
    8. Cabrales, Antonio & Gossner, Olivier & Serrano, Roberto, 2017. "A normalized value for information purchases," Journal of Economic Theory, Elsevier, vol. 170(C), pages 266-288.
    9. Chanel, Olivier, 1995. "Is art market behaviour predictable?," European Economic Review, Elsevier, vol. 39(3-4), pages 519-527, April.
    10. Marie BLUM, 2021. "Which price, if we all like it? Effects of liking and emotional consensus on art prices in auctions," Working Papers of LaRGE Research Center 2021-11, Laboratoire de Recherche en Gestion et Economie (LaRGE), Université de Strasbourg.
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