This paper examines point and density forecasts from the European Central Bank's Survey of Professional Forecasters. We derive individual uncertainty measures along with individual point- and density-based measures of disagreement. We also explore the relationship between uncertainty and...

We have studied the relationship between Receiver Operating Characteristics (ROC) and Precision Recall Curve (PRC) both analytically and using a real-life empirical example of yield spread as a predictor of recessions. We show that false alarm rate in ROC and inverted precision in PRC are...

In this research note we report on our current efforts on developing a leading indicator of housing prices that could be used to forecast housing prices. Specifically we use Google search index at city level to predict Case-Shiller index. The methodology is based on Granger causality where we...

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...

When searching the Finnish Liiga website for statistics, I discovered an interesting statistic called Corsi%-C. It is defined as “...when the score is within a goal in the first two periods and tied in the third period”. (liiga, 2021) Essentially, this is an enhanced version of Corsi for...

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...

This study seeks to answer whether it is possible to design an early warning system framework that can signal the risk of fiscal stress in the near future, and what shape such a system should take. To do so, multiple models based on econometric logit and the random forest models are designed and...

We develop early warning models for financial crisis prediction by applying machine learning techniques to macrofinancial data for 17 countries over 1870–2016. Most nonlin-ear machine learning models outperform logistic regression in out-of-sample predictions and forecasting. We identify...

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...