Research on liquidity and valuation risk assessment of art financial assets based on deep learning.

Art is increasingly recognized as an alternative asset class in wealth management. However, its non-standardized nature renders it difficult to treat on a par with conventional financial assets. Valuation risk arises from subjective pricing and expert bias, while liquidity risk is manifested in high auction failure rates. These challenges are difficult to address adequately with traditional linear econometric models. This study proposes a multimodal deep learning framework that integrates painting images, historical transaction data, artist reputation, and macroeconomic indicators. A ResNet-50 network, initialized on ImageNet and subsequently adapted to the artwork domain, serves as the visual encoder, while a multi-layer perceptron processes structured tabular variables; the two representations are combined in a fusion layer. The framework simultaneously performs two risk-assessment tasks: valuation deviation regression and unsold-lot binary classification. The model is estimated on 96,314 modern and contemporary auction lots offered between 2000 and 2023, of which 27.8% went unsold, using a strictly chronological training, validation, and test partition. The empirical results demonstrate that the proposed multimodal framework outperforms traditional benchmarks, including ordinary least squares (OLS), logistic regression, and random forests. The model achieves an Area Under the Curve (AUC) of 0.891 for liquidity risk prediction. It reduces the Root Mean Squared Error (RMSE) for valuation deviation by 32.9% compared to OLS. Ablation studies confirm that visual embeddings provide substantial incremental information beyond what can be attributed to increased model complexity alone, and permutation importance combined with linear probing shows that the visual representation encodes stylistic period, subject type, chromatic intensity, and compositional complexity-the properties through which visual content is theorized to affect market outcomes. Robustness checks further delineate the model's performance boundaries under macroeconomic shocks. This study translates the aesthetic heterogeneity of non-standard assets into quantifiable risk signals. It offers financial institutions a data-calibrated basis for adjusting dynamic loan-to-value ratios and assists art funds in optimizing portfolio allocation.