Advanced machine learning approaches for bankruptcy prediction in the shipping industry: a comparative analysis across time horizons

This study evaluates machine learning methods for predicting bankruptcies in the Korean shipping industry over 1-, 3-, and 5-year horizons. Using an imbalanced dataset, preprocessing techniques such as outlier capping, skewness correction, and SMOTE-based oversampling are applied. Two feature sets—financial variables and those enriched with macroeconomic and shipping indicators—are analyzed. Results demonstrate that ensemble methods like LightGBM, CatBoost, XGBoost outperform linear models and deep neural networks across all horizons. Short-term predictions are driven by liquidity factors, while long-term forecasts benefit from shipping-specific indices like freight rates. SHAP‐based analysis reveals that internal cash‐flow as critical for one‐year predictions, whereas prolonged freight rate declines and financing pressures dominate longer horizons. These findings highlight the importance of horizon‐specific modeling, support the adoption of advanced machine learning in maritime risk assessment, and encourage further exploration of sector-specific features to improve predictive accuracy and resilience in the shipping industries.