Quantitative Finance / q-fin.GN

Deep Learning for Art Market Valuation

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📝 Original Info

  • Title: Deep Learning for Art Market Valuation
  • ArXiv ID: 2512.23078
  • Date: 2025-12-28
  • Authors: Jianping Mei, Michael Moses, Jan Waelty, Yucheng Yang

📝 Abstract

We study how deep learning can improve valuation in the art market by incorporating the visual content of artworks into predictive models. Using a large repeated-sales dataset from major auction houses, we benchmark classical hedonic regressions and tree-based methods against modern deep architectures, including multi-modal models that fuse tabular and image data. We find that while artist identity and prior transaction history dominate overall predictive power, visual embeddings provide a distinct and economically meaningful contribution for fresh-to-market works where historical anchors are absent. Interpretability analyses using Grad-CAM and embedding visualizations show that models attend to compositional and stylistic cues. Our findings demonstrate that multi-modal deep learning delivers significant value in precisely the situations where valuation is hardest-first-time sales-and thus offers new insights for both academic research and practice in art market valuation.

📄 Full Content

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