PREDICTIVE AI FOR ART INVESTMENT AND VALUATION - Babu, Harshith; Mankad, Kairavi; Srivastava, Swati; Sarkar, Sudeshna; Guntaj J; Lahane, Payal Sunil (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The conventional approach to art valuation has been based on the subjective judgment of experts and piecemeal market information, which have tended to create inconsistent valuation and a lack of transparency. This paper offers a Predictive AI Framework that combines visual, textual, behavioral and financial data to predict the valuations of works of art with high precision and readability. The model uses ensemble learning based on Gradient Boosting, Random Forest, and Deep Neural Networks to obtain nonlinear relationships between aesthetic characteristics, sentiment trends and market indicators. The experimental findings reveal that the performance of the experimental models is greatly enhanced when compared to traditional hedonic models because of the reduced MAPE and increased R 2 scores. Integrating the Explainable AI (XAI) gives insights on features at the feature-level and integrated blockchain provenance makes sure that it has authenticity and tracking capability. The results allow concluding that AI-based valuation has potential to effectively mediate the culture perception and financial analytics in an effort to have a clear and data-driven basis of informed art investment and policy development.