Sovereign Debt and Currency Crises Prediction Models Using Machine Learning Techniques

Symmetry

ManuelA. Fernández-Gámez¹  JoséIgnacio Peláez²  M.Belén Salas³  David Alaminos⁴ 

[1] Department of Finance and Accounting, Universidad de Málaga, 29071 Málaga, Spain;Department of Languages and Computer Science, Universidad de Málaga, 29071 Málaga, Spain;PhD Program in Economics and Business, Universidad de Málaga, 29071 Málaga, Spain;PhD Program in Mechanical Engineering and Energy Efficiency, Universidad de Málaga, 29071 Málaga, Spain;

关键词: sovereign debt crisis prediction;    currency crisis prediction;    deep learning neural decision trees;    fuzzy decision trees;    extreme gradient boosting;    country reputation;   

DOI  :  10.3390/sym13040652

来源: DOAJ

【 摘 要 】

Sovereign debt and currencies play an increasingly influential role in the development of any country, given the need to obtain financing and establish international relations. A recurring theme in the literature on financial crises has been the prediction of sovereign debt and currency crises due to their extreme importance in international economic activity. Nevertheless, the limitations of the existing models are related to accuracy and the literature calls for more investigation on the subject and lacks geographic diversity in the samples used. This article presents new models for the prediction of sovereign debt and currency crises, using various computational techniques, which increase their precision. Also, these models present experiences with a wide global sample of the main geographical world zones, such as Africa and the Middle East, Latin America, Asia, Europe, and globally. Our models demonstrate the superiority of computational techniques concerning statistics in terms of the level of precision, which are the best methods for the sovereign debt crisis: fuzzy decision trees, AdaBoost, extreme gradient boosting, and deep learning neural decision trees, and for forecasting the currency crisis: deep learning neural decision trees, extreme gradient boosting, random forests, and deep belief network. Our research has a large and potentially significant impact on the macroeconomic policy adequacy of the countries against the risks arising from financial crises and provides instruments that make it possible to improve the balance in the finance of the countries.

【 授权许可】

Unknown