# Currency Crises Prediction Using Deep Neural Decision Trees
## 1 Introduction
encounters imperfect capital mobility/asset substitutability movements, producing the same result as discretionary monetary policy and without jeopardizing the inflation target [9; 10; 11]. To avoid future crises, researchers have tried to identify common factors underlying exchange rate instability and develop predictive models. However, despite impressive results for in-sample, the existing early warning models encounter difficulties when it comes to predicting crises outside it [12; 13; 14]. In recent years, there has been considerable research on currency crises, mainly on the application of computational techniques for emerging economies. Statistical methods have also been used, albeit with limited success. For example, [PERSON] [12] applied extreme value theory, obtaining an accuracy of 44%. Reference [15] developed a discrete choice early warning system considering the persistence of the phenomenon of the crisis. Their logistic regression system used a maximum likelihood estimation method both country-by-country and in a panel framework. The model obtained predictive capacity that significantly improved the existing static models, both inside and outside the sample (89.8% and 90.2%, respectively). Reference [1] used computational combinations with support vector machine (SVM), logistic regression, and logical analysis of data tree (LADTree), based on the k-nearest neighbor. The results showed that the computational classifiers were more accurate than the traditional statistical methods, obtaining a level above 90%. [PERSON] [2] individually used SVM for the currency crisis in Argentina, obtaining a high level of robustness. Similarly, Reference [13] studied currency crises in developed countries using the classification and regression tree methodology (CART) and random forest (RF). Their findings determined that significant factors included high short-term domestic interest rates and overvalued exchange rates. Reference [16] compared logistic regression, neural networks (NNs), and decision trees (DTs) to predict the currency crisis in Turkey, with NNs achieving the highest level of accuracy. Also for Turkey, Reference [17] used logistic regression to analyze the determinants of the currency and banking crisis. The study found that currency crises are caused by an excessive fiscal deficit, short-term increases in external debt, overvaluation of the Turkish lira, and adverse external shocks, confirming the results obtained by other studies based on the experiences of emerging countries [18; 19]. This study attempted to build more accurate models for predicting currency crises. To do so, we developed models for four regions of the world (i.e., Latin America, Asia, Africa, and the Middle East and Europe) together with a global model for all world regions. This study thus sought to address a gap in the literature, which requires broader models that can provide powerful and homogeneous empirical tools for public institutions in different countries. It did this using the deep neural decision trees (DNDTs) methodology, developed in Reference [20], which allows for solutions to forecasting problems involving data outside of a sample, also one of the least resolved aspects in the existing literature. We compared this novel method in terms of accuracy with other popular methodologies used in time-series prediction such as regression logistic, neural networks, support vector machines, and AdaBoost. The rest of this article is organized as follows: Section 2 describes the DNDTs algorithm, and Section 3 summarizes the data and variables used as possible predictors. The results and their comparison with the existing literature are provided in Section 4. Finally, Section 5 summarizes the main conclusions.