Global investing risk: a case study of knowledge assessment via rough sets

Abstract

This paper presents an application of knowledge discovery via rough sets to a real life case study of global investing risk in 52 countries using 27 indicator variables. The aim is explanation of the classification of the countries according to financial risks assessed by Wall Street Journal international experts and knowledge discovery from data via decision rule mining, rather than prediction; i.e. to capture the explicit or implicit knowledge or policy of international financial experts, rather than to predict the actual classifications. Suggestions are made about the most significant attributes for each risk class and country, as well as the minimal set of decision rules needed. Our results compared favorably with those from discriminant analysis and several variations of preference disaggregation MCDA procedures. The same approach could be adapted to other problems with missing data in data mining, knowledge extraction, and different multi-criteria decision problems, like sorting, choice and ranking.

Sovereign Credit Risk Assessment with Multiple Criteria Using an Outranking Method

“…In this application, countries are initially divided into categories that are defined according to the income level of the alternatives: a high-income economy, a middlehigh income economy, a low-middle income economy, and a low-income economy. Greco et al [41] evaluated, by using decision rules in a learning approach via Rough Sets, the risk of investing in a set of 52 countries, based on 27 indicators.…”

“…A search for possible financial indicators was carried out and nine criteria that the World Bank measures and publishes were collected. Some examples of financial indicators used to evaluate countries can be found in literature [40,41]. The criteria chosen and used in the model are shown in Table 1.…”

A new decision-making approach for multiple criteria sorting with an imbalanced set of assignment examples

“…Although existing MCS methods have shown great success in many real-world applications [26,27,28,29,30,31,32,33,34,35,36,37,38], the complexity of some practical problems makes them inadequate to achieve a satisfactory assignment recommendation. One noteworthy difficulty is the imbalanced distribution of alternatives among considered categories, which exists in a wide range of real-world applications.…”