A Machine Learning Approach: Financial Crisis Forecasting in G7 Countries

Abstract

This study investigates the effectiveness of machine learning methods in predicting systemic banking crises, particularly using the XGBoost algorithm, which stands out for its ability to capture complex and non-linear patterns. A XGBoost-based model was developed to predict systemic banking crises using financial and macroeconomic data from G7 countries for the period 1870-2020. Additionally, SHAP (SHapley Additive exPlanations) methods were applied to analyze the causal relationships between model results, thereby overcoming the model's “black box” nature and providing a deeper understanding of decision-making processes. This allowed for a transparent analysis of the causal relationships between predictive variables and crisis risk. The findings demonstrate that XGBoost exhibits high predictive performance, offering new opportunities for practitioners and policymakers in assessing crisis risk. Additionally, SHAP values significantly enhance the transparency and accountability of machine learning models by revealing the complex relationships between predictive variables and crisis risk. This approach provides a robust and reliable analytical framework for identifying the fundamental economic drivers of financial crises, highlighting the potential of machine learning methods in financial crisis prediction.

Keywords

• Financial Crises
• Machine Learning
• XGBoost
• SHAP Value
• Forecasting

Bir Makine Öğrenimi Uygulaması: G7 Ülkelerinde Finansal Kriz Tahminleme

Abstract

Bu çalışma, sistemik bankacılık krizlerini tahmin etmede makine öğrenmesi yöntemlerinin etkinliğini, özellikle karmaşık ve doğrusal olmayan örüntüleri yakalama yeteneğiyle öne çıkan XGBoost algoritmasını kullanarak araştırmaktadır. 1870-2020 dönemi için G7 ülkelerine ait finansal ve makroekonomik veriler kullanılarak sistemik bankacılık kriz tahmininde XGBoost tabanlı bir model geliştirilmiştir. Ayrıca, modelin 'kara kutu' doğasını aşarak karar alma süreçlerini derinlemesine anlamlandırmak amacıyla SHAP (SHapley Additive exPlanations) yöntemleri uygulanarak model sonuçları arasındaki nedensel ilişkiler analiz edilmiş, böylece tahmin edici değişkenler ile kriz riski arasındaki nedensel ilişkiler şeffaf bir şekilde analiz edilmiştir. Bulgular, XGBoost'un yüksek tahmin performansı sergileyerek uygulayıcılar ve politika yapıcılar için kriz riskini değerlendirmede yeni olanaklar sunduğunu göstermektedir. Ek olarak SHAP değerleri, tahmin edici değişkenler ile kriz riski arasındaki karmaşık ilişkileri ortaya çıkararak makine öğrenimi modellerinin şeffaflığını ve hesap verebilirliğini önemli ölçüde artırmaktadır. Bu yaklaşım, finansal krizlerin temel ekonomik itici güçlerini belirleme konusunda sağlam ve güvenilir bir analitik altyapı sunarak finansal kriz tahmininde makine öğrenmesi yöntemlerinin potansiyelini vurgulamaktadır.

Keywords

• Finansal Krizler
• Makine Öğrenimi
• XGBoost
• SHAP Değeri
• Tahminleme

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