To estimate the probability of default of companies and the correlated rating classes, it is necessary to use efficiently the information contained in different databases. In this respect, we propose a novel approach, based on the recursive usage of Bayes theorem, that can be very helpful in...

Twitter text data may be very useful to predict financial tangibles, such as share prices, as well as intangible assets, such as company reputation. While twitter data are becoming widely available to researchers, methods aimed at selecting which twitter data are reliable are, to our knowledge,...

The measurement of the quality of academic research is a rather controversial issue. Recently Hirsch has proposed a measure that has the advantage of summarizing in a single summary statistics all the information that is contained in the citation counts of each scientist. From that seminal...

In this contribution we aim at improving ordinal variable selection in the context of causal models. In this regard, we propose an approach that provides a formal inferential tool to compare the explanatory power of each covariate, and, therefore, to select an effective model for classification...

Operational risk is hard to quantify, for the presence of heavy tailed loss distributions. Extreme value distributions, used in this context, are very sensitive to the data, and this is a problem in the presence of rare loss data. Self risk assessment questionnaires, if properly modelled, may...

In this paper we propose a novel approach to measure risks, when the data available are expressed in an ordinal scale. As a result we obtain a new index of risk bounded between 0 and 1, that leads to a risk ordering that is consistent with a stochastic dominance approach. The proposed measure,...