A global prediction model for sudden stops of capital flows using decision trees

• Published in: PloS one Vol. 15; no. 2; p. e0228387
• Main Authors: Salas, M Belén, Alaminos, David, Fernández, Manuel Angel, López-Valverde, Francisco
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 12.02.2020; Public Library of Science (PLoS)
• Subjects: Accuracy; Adequacy; Algorithms; Analysis; Computer and Information Sciences; Decision making; Decision Trees; Developed countries; Developed Countries - economics; Developing countries; Developing Countries - economics; Emerging markets; Engineering and Technology; Finance; Financial Management - statistics & numerical data; Foreign exchange rates; GDP; Gross Domestic Product; Heterogeneity; Humans; Industrialized countries; Industrialized nations; International finance; Literature reviews; Model accuracy; Models, Theoretical; Monetary systems; Open economy; Physical Sciences; Prediction models; Research and Analysis Methods; Retirement benefits; Social Sciences; Studies; Time; Spain
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0228387

Capital flows is an important aspect of the international monetary system because they provide great direct and indirect benefits, and at the same time, they carry risks of vulnerability for countries with an open economy. Numerous works have studied the behavior of these flows and have developed models to predict sudden stop events. However, the existing models have limitations and the literature demands more research on the subject given that the accuracy of the models is still poor, and they have only been developed for emerging countries. This paper presents a new prediction model of sudden stop events of capital flows for both emerging countries and developed countries with the ability to estimate accurately future sudden stop scenarios globally. A sample of 103 countries was used, including 73 emerging countries and 30 developed countries, which has allowed the use of sample combinations that consider the regional heterogeneity of the warning indicators. To the sample under study, a method of decision trees has been applied, which has provided excellent prediction results given its ability to learn characteristics and create long-term dependencies from sequential data and time series. Our model has a great potential impact on the adequacy of macroeconomic policy against the risks derived from sudden stops of capital flows, providing tools that help to achieve financial stability at the global level.