Modeling Investment Strategies in Contemporary Art in the Context of the Yugoslav Wars

This research advances a methodological framework for developing predictive models to guide investment strategies in contemporary art across the post-Yugoslav Wars Balkan region. The study examines key determinants of artwork valuation and risk profiles in Bosnia and Herzegovina, Croatia, and Serbia using a mixed-methods approach that synthesizes meta-analysis, expert Q-sort analysis, and hierarchical cluster analysis. The empirical findings demonstrate the enduring influence of the Yugoslav Wars on art market dynamics, underscoring the significance of sustainable intelligence principles and technological innovation in risk mitigation strategies. The analysis identifies critical valuation determinants, including artistic recognition, sociopolitical stability, and cultural significance, while establishing distinct market segments characterized by differentiated risk-reward profiles. The proposed framework incorporates geopolitical risk indicators, artwork and artist attributes, sustainable investing criteria, and technological enablers, emphasizing machine learning algorithms for market forecasting and portfolio optimization. While this framework constitutes a substantial advancement in modelling art investment strategies within post-conflict contexts, limitations persist regarding data availability, model interpretability, and ethical considerations. The study demonstrates the framework's potential to generate value while contributing to cultural heritage preservation and socioeconomic development in the Balkan region, establishing a foundation for subsequent scholarly inquiry into art market analysis within post-conflict societies.