IDEAS home Printed from https://ideas.repec.org/p/inf/wpaper/2025.6.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: https://infer-research.eu/wp-content/uploads/2025/05/WP2025.06.pdf
    File Function: First version, 2025
    Download Restriction: no
    ---><---

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inf:wpaper:2025.6. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pedro Cerqueira The email address of this maintainer does not seem to be valid anymore. Please ask Pedro Cerqueira to update the entry or send us the correct address (email available below). General contact details of provider: https://edirc.repec.org/data/inferea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.