Asymmetric Effects of Corporate Governance on Financial Distress: A Granular Computing and Explainable Machine Learning Approach in Borsa Istanbul
Keywords:
Corporate governance, Financial distress, Explainable machine learning, XGBoost, SHAP, Granular computingAbstract
We examine whether corporate governance indicators predict financial distress among 200 non-financial firms listed on Borsa Istanbul, using governance data from 2023 and financial outcomes from 2024, a year of severe macroeconomic stress in Türkiye. Naive designs inflate performance through label leakage and impose linearity on governance effects. We address both problems with a leakage-free nested cross-validation protocol, two distinct outcomes, and explainable machine learning. The first outcome is a narrowly defined new operating-loss onset, a firm that is not distressed in 2023 and reports negative operating profit in 2024 under the composite distress rule. The second outcome is the 2024 distress state conditional on the 2023 state and serves as a persistence benchmark. SHAP attributions are paired with formal segmented-regression threshold tests. The results identify a clear onset-persistence asymmetry. The onset models are estimated on the 155 firms at risk of onset, and under internal validation the onset classifiers produce mean AUC values between 0.54 and 0.65. The parsimonious specifications display moderate discrimination, but most interval estimates span 0.5 and the evidence remains weak. The onset outcome is entirely composed of new operating-loss cases in this sample. The conditional state model performs well, with AUC between 0.77 and 0.81, but its performance is dominated by the baseline distress state, whose average marginal effect is 0.413. The full governance block attains a grouped SHAP attribution of 0.130, far below the financial block. Dependence plots suggest threshold-like patterns, yet no formal break is detected, with no likelihood-ratio test reaching significance at the five percent level. An earlier naive specification reaches an AUC of 0.727 but changes the label, clustering and validation at the same time, so it is retained only to illustrate how evaluation design inflates performance. The study contributes by showing that separating distress onset from persistence and enforcing time-consistent validation yields a more credible basis for assessing the predictive value of governance measures in emerging-market firms.
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