Information extraction and artwork pricing

TL;DR

This paper introduces a novel content measurement for artwork pricing using SVD entropy, demonstrating its positive impact on sales prices and advantages over traditional variables in robustness and model fit.

Contribution

It proposes a new content measurement method based on SVD entropy for artwork pricing, showing its effectiveness and advantages over existing variables.

Findings

SVD entropy positively influences artwork sales prices.

SVD entropy outperforms traditional content variables in robustness and significance.

The measurement is easy to compute and widely applicable.

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.