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...

Forecasting commodities and especially oil prices has attracted significant research interest, often concluding that oil prices are not easy to forecast and implying an efficient market. In this paper, we revisit the efficient market hypothesis of the oil market attempting to forecast the West...

The issue of whether or not money affects real economic activity (money neutrality) has attracted significant empirical attention over the last five decades. If money is neutral even in the short-run, then monetary policy is ineffective and its role limited. If money matters, it will be able to...

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...

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...