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Do International Reserve Holdings Still Predict Economic Crises? Insights from Recent Machine Learning Techniques

Author

Listed:
  • Nikolaos Giannakis

    (Democritus University of Thrace)

  • Periklis Gogas

    (Democritus University of Thrace)

  • Theophilos Papadimitriou

    (Democritus University of Thrace)

  • Jamel Saadaoui

    (University Paris 8)

  • Emmanouil Sofianos

    (University of Strasbourg)

Abstract

This study aims to predict currency, banking, and debt crises using a dataset of 184 crisis events and 2896 non-crisis cases from 79 countries (1970-2017). We tested eight machine learning methods: Logistic Regression, KNN, SVM, Random Forest, Balanced Random Forest, Balanced Bagging Classifier, Easy Ensemble Classifier, and Gradient Boosted Trees. The Balanced Random Forest had the best performance with a 72.91% balanced accuracy, predicting 149 out of 184 crises accurately. To address machine learning’s black-box issue, we used Variable Importance Measure (VIM) and Partial Dependence Plots (PDP). International reserve holdings, inflation rate, and current account balance were key predictors. Depleting international reserves at varying inflation levels signals impending crises, supporting the buffer effects of international reserves.

Suggested Citation

  • Nikolaos Giannakis & Periklis Gogas & Theophilos Papadimitriou & Jamel Saadaoui & Emmanouil Sofianos, 2025. "Do International Reserve Holdings Still Predict Economic Crises? Insights from Recent Machine Learning Techniques," Working Papers 2025.6, International Network for Economic Research - INFER.
    • Handle: RePEc:inf:wpaper:2025.6
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Currency crises; banking crises; debt crises; international reserve holdings; inflation; machine learning; forecasting;
    All these keywords.

    JEL classification:

    • F - International Economics
    • G - Financial Economics

    NEP fields

    This paper has been announced in the following NEP Reports:

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