Dynamic Sustainability Risk: An Artificial Intelligence Framework for Explaining Forward-Looking Industry Betas

Systematic risk plays a central role in company valuation, enterprise risk management, and sustainable investment. However, conventional approaches primarily rely on historical beta estimates and provide limited insight into the factors associated with future changes in systematic risk. This study develops an AI-supported framework for identifying the determinants of one-year-ahead industry beta coefficients for the US economy by combining macroeconomic variables with risk indicators derived from global news analytics. Annual industry betas published by Damodaran are transformed into monthly observations to align with lagged explanatory variables. The analysis combines macroeconomic indicators with twenty-two artificial intelligence-supported risk categories extracted from the GDELT database, collectively representing Dynamic Sustainability Risk. The empirical results show that historical beta persistence alone does not fully explain future industry beta coefficients. Sustainability-related factors—including ESG, supply-chain, technological, strategic, and labor-market risks—consistently appear among the significant determinants across industries, complementing traditional macroeconomic variables. Furthermore, forward-looking systematic risk is associated with interactions between macroeconomic conditions and dynamic sustainability-related risks rather than with historical financial information alone. Rather than developing a forecasting model, the proposed framework provides an interpretable approach for identifying the macroeconomic and sustainability-related determinants associated with future industry beta coefficients, thereby supporting company valuation, enterprise risk management, and sustainable financial decision-making.