Machine Learning Validation of Alaali Cash Flow Volatility Index–ESG Extended Model (A-CFVI-ESG-X)

This report details the machine learning validation process of the Alaali Cash Flow Volatility Index–ESG Extended (A-CFVI-ESG-X) model, part of the Alaali Financial Models Framework (AFMF). The validation leverages logistic regression on simulated firm-level data to assess the model's accuracy and predictive capabilities in detecting financial fragility driven by ESG shocks. The A-CFVI-ESG-X model enhances baseline cash flow volatility assessments by integrating ESG-related risks and amplification mechanisms, positioning it as a practical tool for forward-looking financial risk analysis and ESG-sensitive risk management. The report provides a summary of key validation metrics and identifies influential ESG-driven predictors, paving the way for future real-world application and comparative analyses against traditional financial metrics. Keywords: A-CFVI-ESG-X, Machine Learning Validation, ESG Integration, Financial Volatility, Risk Management, Logistic Regression, Alaali Financial Models Framework, ESG Shocks, Predictive Analytics, Financial Stability, Financial Risk Modeling