Deep Learning for Art Market Valuation

Jianping Mei, Michael Moses, Jan Wàlty and Yucheng Yang
Additional contact information
Jianping Mei: Cheung Kong Graduate School of Business
Michael Moses: Art Market Consultancy
Jan Wàlty: University of Zurich
Yucheng Yang: University of Zurich; Swiss Finance Institute

No 25-110, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

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 forboth academic research and practice in art market valuation.

Pages: 45 pages
Date: 2025-12
References: Add references at CitEc
Citations:

Downloads: (external link)
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5988754 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp25110

Access Statistics for this paper

More papers in Swiss Finance Institute Research Paper Series from Swiss Finance Institute Contact information at EDIRC.
Bibliographic data for series maintained by Ridima Mittal ().