Country Risk Prediction with Machine Learning Techniques

Seyyide DOÄžAN and Hasan TÃœRE

Fiscaoeconomia, 2022, issue 3

Abstract: Country risk assessment, in the most general sense, is a measure of the foreign aid a country can receive and the risk the investors will face. Therefore, the related risk has to be measured by making rather sensitive predictions with a procedure where economical, financial and political risks are taken into account. The prediction method must be chosen with great accurateness and definitely supported with different methods. To that end, LRA, KNN, CART and DVM methods, which produce good estimation result and frequently used, are preferred in country risk predictions. Different macroeconomic indicators of 75 countries between the years 2015 and 2019 are used to train the prediction model. According to the findings of the study, it can be said that quite successful prediction results are produced with all the chosen methods. When different assessment criteria are taken into account and each machine learning algorithm are repeated 100 times, it is seen that the KNN algorithm is the best method to produce results. The following methods can be arrayed as DVM, LRA and CART.

Keywords: country risk; machine learning; support vector machine; k-nearest neighbor; logistic regression; decision trees (search for similar items in EconPapers)
JEL-codes: C21 C45 (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:fis:journl:220309

DOI: 10.25295/fsecon.1098493

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