AI Semiconductor Valuation: Industry Dynamics and Investment Implications

Description This research investigates how artificial intelligence (AI) reshapes the valuation logic of semiconductor firms through the lens of dynamic capabilities and resource-based theory. Using econometric and AI-assisted modeling, the study integrates Tobin’s Q, R&D intensity, patent activity, and global market factors to explore how strategic agility and open innovation jointly influence firm competitiveness in Taiwan’s semiconductor ecosystem. Empirical analyses adopt the Generalized Estimating Equations (GEE) framework to address longitudinal effects and inter-firm heterogeneity, supported by feature-importance analytics that quantify the predictive contribution of key innovation and capability variables. The study’s findings reveal that the integration of AI-driven strategic agility and resource-based dynamic capabilities significantly enhances valuation resilience in high-uncertainty technology sectors. This independent research contributes to the literature on technology strategy, AI economics, and industrial competitiveness. It provides actionable insights for policy makers, investors, and corporate strategists seeking to evaluate how emerging technologies alter industrial value creation and investment dynamics. Supplementary Materials:The uploaded Python code (valuation_model_code.zip) contains econometric modeling scripts and AI-based feature-importance analysis for reproducibility of the research results.