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Journals / Uluslararası Ekonomi İşletme ve Politika Dergisi / 2026 / Cilt: 9 Sayı: 1

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

Tekstil Endüstrisinde Finansal Sıkıntının Tahmini: Meta Modellerin ve Tek Sınıflandırıcıların Karşılaştırmalı Analizi

Ahmet Akusta(KONYA TEKNİK ÜNİVERSİTESİ)0000-0002-5160-3210
Musa Gün(Recep Tayyip Erdoğan Üniversitesi)0000-0002-5020-9342
Pages
20–36
Publication Date
2025-04-15
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study evaluates the effectiveness of meta-models in predicting financial distress in the Turkish textile industry.
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Key Findings & Data: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.
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Conclusion & Impact: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.
🔬Design:Modeling & Simulation
📍Scope:2013 Verileri
⚙️Variables:Finansal Sıkıntı Tahmini · Makine Öğrenmesi · Özellik Mühendisliği

Abstract

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.

Özet

Bu çalışma, Türk tekstil sektöründeki finansal sıkıntıları tahmin etmede meta modellerin etkinliğini değerlendirmektedir. Araştırma, 2013'ten 2023'e kadar olan finansal verileri kullanarak Kement, Ridge, Rastgele Orman, Gradyan Arttırma Makineleri (GBM) ve Destek Vektör Makinelerini (DVM) temel modeller olarak entegre eden ve XGBoost'un meta öğrenici olarak hizmet ettiği bir meta model uygulamaktadır. Sonuçlar, meta modelin, özellikle finansal sıkıntının erken tespiti için kritik olan yanlış negatifleri en aza indirmede bağımsız bir XGBoost sınıflandırıcıdan daha iyi performans gösterdiğini göstermektedir. Meta model, tekstil gibi değişken sektörlerde finansal istikrarsızlığı tahmin etmek için daha güvenilir bir araç sunarak üstün hatırlama ve F1 puanları elde etmiştir. Bununla birlikte, çalışma aynı zamanda model seçimi yanlılığı, hiperparametre ayarının karmaşıklığı ve yaklaşımın topluluk doğası nedeniyle yorumlanabilirliğin azalması gibi sınırlamaları da kabul etmektedir. Bulgular, meta modellemenin sektöre özgü finansal risk tahmini için potansiyelini vurgularken, model şeffaflığı ve genelleştirilebilirliğinde gelecekte yapılabilecek iyileştirmelere dair önerilerde bulunmaktadır.

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Cite This Article

Akusta, A., Gün, M. (2025). Predicting Financial Distress in The Textile Industry: A Comparative Analysis of Meta Models and Single Classifiers. Uluslararası Ekonomi İşletme ve Politika Dergisi, Cilt: 9 Sayı: 1, 20–36. https://doi.org/10.29216/ueip.1599431

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