Paper: arXiv 2302.08167
Authors: 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.
Complexity vs Empirical Score
- Math Complexity: 4.5/10
- Empirical Rigor: 6.5/10
- Quadrant: Street Traders — practical and empirical, lighter on theory
Why this score: The paper introduces a novel application of Shannon information theory and SVD entropy to quantify painting content for artwork pricing. While mathematically straightforward, it demonstrates strong empirical utility by outperforming traditional content variables across a large auction dataset.
Research Flowchart
flowchart TD
A[Research Goal: Improve Art Pricing Models via Digital Image Content] --> B[Data Inputs: Auction Records & Digital Painting Images]
B --> C[Compute SVD Entropy: Calculate Shannon Information via Singular Value Decomposition]
C --> D[Hedonic Regression Analysis: Compare SVD Entropy vs. Traditional Content Variables]
D --> E[Key Outcome: SVD Entropy Positively Impacts Price at 1% Significance]
D --> F[Key Outcome: Superior Model Fit, Variable Significance & Sample Robustness]