<![CDATA[Objective: This study aims to examine the theoretical foundations and methodologies of the Country Risk Assessment Model (CRAM), focusing on its ability to evaluate country risk through a comprehensive analysis of economic, political, and social indicators. Method: CRAM employs a weighted scoring system, integrating indicators from economic, political, and social domains to construct an aggregate risk score. Data sources include the IMF, World Bank, PRS Group, Transparency International, and the UNDP. Weights for each indicator are derived through regression analysis, expert surveys, and historical correlations with adverse events. Results: The model’s composite index provides a nuanced and multi-dimensional risk assessment, offering valuable insights for investment decisions and forecasting. However, CRAM’s reliance on historical data may limit its responsiveness to rapidly changing risk factors such as political upheavals or sanctions. Novelty: This study highlights the potential for enhancing CRAM by incorporating machine learning techniques to dynamically adjust indicator weights, as well as integrating sub-national data for more precise assessments in large, diverse countries. These improvements could enhance CRAM's predictive accuracy and maintain its relevance in an evolving global risk landscape.]]>
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