The 2006 sudden and immense downturn in U.S. House Prices sparked the 2007 global financial crisis and revived the interest about forecasting such imminent threats for economic stability. In this paper we propose a novel hybrid forecasting methodology that combines the Ensemble Empirical Mode...

Purpose – This study aims to present an empirical model designed to forecast bank credit ratings using only quantitative and publicly available information from their financial statements. For this reason, the authors use the long-term ratings provided by Fitch in 2012. The sample consists of...

We propose a support vector machine (SVM)-based structural model to forecast the collapse of banking institutions in the USA using publicly disclosed information from their financial statements on a four-year rolling window. In our approach, the optimum input variable set is defined from a large...

In this study we compare the forecasting ability of the simple sum and Divisia monetary aggregates with respect to U.S. gross domestic product. We use two alternative Divisia aggregates, the series produced by the Center for Financial Stability (CFS Divisia) and the ones produced by the Federal...

In this paper, we approximate the empirical findings of Papadamou and Markopoulos (2012) on the NOK/USD exchange rate under a Machine Learning (ML) framework. By applying Support Vector Regression (SVR) on a general monetary exchange rate model and a Dynamic Evolving Neuro-Fuzzy Inference System...

We empirically test the effects of unanticipated fiscal policy shocks on the growth rate and the cyclical component of real private output and reveal different types of asymmetries in fiscal policy implementation. The data used are quarterly U.S. observati ons over the period 1967:1 to 2011:4....

In this study we compare the forecasting ability of the simple sum and Divisia monetary aggregates with respect to U.S. gross domestic product. We use two alternative Divisia aggregates, the series produced by the Center for Financial Stability (CFS Divisia) and the ones produced by the Federal...

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...

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...