The accurate forecast of the foreign currencies exchange rates at the ultra high frequency electronic trading in the foreign currencies exchange markets is a main topic of our research: 1) the present state of the foreign currencies exchange markets in Asia, Europe and North America; 2) the...

We generalize the refinement ordering for well calibrated probability forecasters to the case were the debtors under consideration are not necessarily identical. This ordering is consistent with many well known skill scores used in practice. We also add an illustration using default predictions...

Forecast combination has been proven to be a very important technique to obtain accurate predictions for various applications in economics, finance, marketing and many other areas. In many applications, forecast errors exhibit heavy-tailed behaviors for various reasons. Unfortunately, to our...

Binary events are involved in many economic decision problems. In recent years, considerable progress has been made in diverse disciplines in developing models for forecasting binary outcomes. We distinguish between two types of forecasts for binary events that are generally obtained as the...

We develop early warning models for financial crisis prediction using machine learning techniques on macrofinancial data for 17 countries over 1870–2016. Machine learning models mostly outperform logistic regression in out-of-sample predictions and forecasting. We identify economic drivers of...

This paper contributes to the old theme of testing for rationality of inflation expectations in surveys, using two very different surveys in parallel. Focusing on the euro area and using two well-known surveys that include questions on inflation expectations, the Consensus Forecast survey and...

It is a common understanding that bankruptcy is not a sudden occurrence for any organizations. Macro and micro economic studies have suggested numerous influential factors, which have substantial evidence toward firm's performance (Bekeris, 2012) and survivability (Nehrebecka & Dzik, 2013). With...

Our paper aims to evaluate two novel methods on selecting the best forecasting model or its combination based on a Machine Learning approach. The methods are based on the selection of the "best" model, or combination of models, by cross-validation technique, from a set of possible models. The...

The complexity of machine learning models presents a substantial barrier to their adoption for many investors. The algorithms that generate machine learning predictions are sometimes regarded as “black box”, demanding interpretation and additional explanation. In this paper, we present a...