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Harris Hawks Optimization-Tuned Extreme Gradient Boosting for Bankruptcy Prediction in Emerging Market Economies.

Narcisa Zlatan
Volume 5

Abstract

 

Bankruptcy prediction in emerging market economies is a complex and high-stakes financial classification problem due to unstable macroeconomic conditions, limited disclosure quality, market inefficiencies, currency fluctuations, liquidity constraints, and the nonlinear interaction between firm-level financial indicators and external economic shocks. Traditional statistical models and standalone machine learning approaches often struggle to capture these complex relationships, particularly when bankruptcy cases are rare, datasets are imbalanced, and firm behavior varies significantly across sectors and economic cycles. This paper proposes a Harris Hawks Optimization-tuned Extreme Gradient Boosting framework for accurate and interpretable bankruptcy prediction in emerging market economies. The proposed framework integrates the strong nonlinear learning capability of XGBoost with the global search efficiency of Harris Hawks Optimization to automatically tune key hyperparameters, including learning rate, maximum tree depth, number of estimators, subsampling ratio, column sampling ratio, regularization parameters, minimum child weight, and gamma. The framework was evaluated using firm-level financial datasets from emerging markets, incorporating accounting ratios, profitability indicators, liquidity measures, leverage ratios, solvency metrics, cash-flow variables, firm size, market-based indicators, sectoral attributes, and macroeconomic variables such as inflation, interest rates, exchange rate volatility, GDP growth, and credit conditions. A comprehensive preprocessing pipeline was applied, including missing value imputation, outlier treatment, winsorization of extreme financial ratios, feature normalization, categorical encoding, correlation analysis, and imbalance-aware learning using SMOTE and cost-sensitive loss weighting. Experimental results demonstrate that the proposed HHO-XGBoost framework achieves superior predictive performance compared with traditional bankruptcy models, including Altman Z-score, logistic regression, discriminant analysis, Support Vector Machine, Random Forest, standalone XGBoost, PSO-XGBoost, and GWO-XGBoost baselines. The optimized model records higher accuracy, sensitivity, specificity, F1-score, AUC-ROC, and precision-recall AUC, while significantly improving the detection of financially distressed firms and reducing false negatives. Furthermore, SHAP-based explainability analysis was incorporated to identify the most influential bankruptcy risk factors, revealing the dominant role of leverage intensity, declining profitability, weak liquidity, negative operating cash flow, high debt servicing burden, reduced asset turnover, and adverse macroeconomic conditions. The proposed framework provides a robust, scalable, and transparent decision-support tool for banks, investors, auditors, regulators, and policymakers, enabling earlier identification of corporate distress, improved credit risk assessment, and stronger financial stability monitoring in emerging market environments.

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Keywords

References

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