The purpose of this paper is to examine the effectiveness of the policies and procedures towards economic convergence between the countries that participated in the European Exchange Mechanism I and which are now members states of the Eurozone. The question posed is whether the introduction of...

We combine signal processing to machine learning methodologies by introducing a hybrid Ensemble Empirical Mode Decomposition (EEMD), Multivariate Adaptive Regression Splines (MARS) and Support Vector Regression (SVR) model in order to forecast the monthly and daily Euro (EUR)/United States...

The difficulty in modelling inflation and the significance in discovering the underlying data generating process of inflation is expressed in an ample literature regarding inflation forecasting. In this paper we evaluate nonlinear machine learning and econometric methodologies in forecasting the...

We empirically test the validity of four popular monetary exchange rate models under five alternative inflation expectation approximations using the NOK/USD exchange rate. The selection of Norway seems appropriate as it is a small open economy that does not participate in most economic or...

In this paper, we present a forecasting model of bank failures based on machine-learning. The proposed methodology defines a linear decision boundary separating the solvent from the failed banks. This setup generates a novel alternative stress testing tool. Our sample of 1443 U.S. banks includes...

In this paper, we investigate the ability of two popular models to forecast the deviation of GDP from its long-run trend, i.e. inflationary and output gaps. In doing so, we exploit the information provided by the yield curve that is documented in the literature as a good predictor of economic...

Purpose -- This study presents an empirical model designed to forecast bank credit ratings using only quantitative and publicly available information from their financial statements. For this reason we use the long term ratings provided by Fitch in 2012. Our sample consists of 92 U.S. banks and...

In this paper, we investigate the forecasting ability of the yield curve in terms of the U.S. real GDP cycle. More specifically, within a Machine Learning (ML) framework, we use data from a variety of short (treasury bills) and long term interest rates (bonds) for the period from 1976:Q3 to...

We propose an Support Vector Machine (SVM) based structural model in order to forecast the collapse of banking institutions in the U.S. using publicly disclosed information from their financial statements on a four-year rolling window. In our approach, the optimum input variable set is defined...