Predicting Financial Distress in The Textile Industry: A Comparative Analysis of Meta Models and Single Classifiers

This study evaluates the effectiveness of meta-models in predicting financial distress in the Turkish textile industry. Using economic data from 2013 to 2023, the research applies a meta-model that integrates Lasso, Ridge, Random Forest, Gradient Boosting Machines (GBM), and Support Vector Machines (SVM) as base models, with XGBoost serving as the meta learner. The results show that the meta-model outperforms a standalone XGBoost classifier, especially in minimizing false negatives, which is critical for the early detection of financial distress. The meta-model achieved superior recall and F1 scores, offering a more reliable tool for predicting financial instability in volatile sectors like textiles. However, the study also acknowledges limitations such as model selection bias, the complexity of hyperparameter tuning, and reduced interpretability due to the ensemble nature of the approach. The findings highlight the potential of meta-modeling for industry-specific financial risk prediction while suggesting future improvements in model transparency and generalizability.