The Conditional Role of Corporate Governance and Explainable Machine Learning in Predicting Severe Profitability Deterioration: Evidence from the S&P 500—An Early Warning System

Corporate governance does not appear to influence firm risk on its own; its role depends on a firm’s financial condition, becoming most informative when financial signals are mixed or ambiguous. Using a sample of S&P 500 firms, this study tests whether the predictive value of governance variables (board independence, CEO duality, CEO compensation, board gender composition, and independent chair status) is conditional on financial state, with a target defined as severe next-year profitability deterioration. Governance variables alone add almost nothing to prediction, collapsing to majority-class guessing, but combined with financial indicators (profitability, leverage, liquidity, valuation) via interaction terms, they sharpen deterioration forecasts, particularly for firms whose financial position alone does not clearly signal risk. Explainable machine learning models with SHAP decomposition trace this pattern to individual variables, showing governance’s contribution is neither uniform nor intuitive: features linked to stronger oversight sometimes predict higher, not lower, deterioration risk. Machine learning outperforms logistic benchmarks, though gains are sometimes modest, and current profitability remains the strongest predictor throughout. The paper’s main contribution is therefore not the predictive improvement itself, but evidence that financial stress, governance quality, and sector context jointly shape deterioration risk, rather than governance acting as an independent early-warning signal.