Credit ratings patterns for BRICS industrial companies
The main goal of this paper is to study interconnections between credit ratings and financial indicators of industrial companies from BRICS countries. We use method of patterns, one of the modern methods of nonlinear modeling, to identify groups of heterogeneous objects with different influence on ratings. Additionally, in this research, we evaluate Tobit regression model for selected groups and establish some credit rating patterns for the BRICS industrial companies. Our results of Tobin model, may have practical implementation in short-term financial management.
We suggest an econometric model of probability of default based on regular financial disclosures of Russian banks. We also suggest a quantization of the continuous explanatory variables that allows to account for non-linear effects and to achieve superior accuracy compared with regression tree and Bayesian network models estimated over the same sample. The econometric estimates of probability of default are broadly consistent with the historical default frequencies of rated obligors and risk-neutral probabilities of default inferred from credit spreads in a reduced-form model.
In our research, we examine what macroeconomic factors determine and influence the credit cycle. In addition, our study contains four sections with theoretical and empirical parts, in which we describe how to measure credit cycles for developed and developing countries, and then introduce an important measure of the credit gap. Our results show a comparative analysis of credit cycles between different countries with different economic growth, and we have created an econometric model, which shows us the impact of macroeconomic factors according to the credit cycles for developing and developed economies.
A scalable method for mining graph patterns stable under subsampling is proposed. The existing subsample stability and robustness measures are not antimonotonic according to definitions known so far. We study a broader notion of antimonotonicity for graph patterns, so that measures of subsample stability become antimonotonic. Then we propose gSOFIA for mining the most subsample-stable graph patterns. The experiments on numerous graph datasets show that gSOFIA is very efficient for discovering subsample-stable graph patterns.
To large organizations, business intelligence (BI) promises the capability of collecting and analyzing internal and external data to generate knowledge and value, thus providing decision support at the strategic, tactical, and operational levels. BI is now impacted by the “Big Data” phenomena and the evolution of society and users. In particular, BI applications must cope with additional heterogeneous (often Web-based) sources, e.g., from social networks, blogs, competitors’, suppliers’, or distributors’ data, governmental or NGO-based analysis and papers, or from research publications. In addition, they must be able to provide their results also on mobile devices, taking into account location-based or time-based environmental data. The lectures held at the Third European Business Intelligence Summer School (eBISS), which are presented here in an extended and refined format, cover not only established BI and BPM technologies, but extend into innovative aspects that are important in this new environment and for novel applications, e.g., pattern and process mining, business semantics, Linked Open Data, and large-scale data management and analysis. Combining papers by leading researchers in the field, this volume equips the reader with the state-of-the-art background necessary for creating the future of BI. It also provides the reader with an excellent basis and many pointers for further research in this growing field.
A brief overview of the results of the classification of the RF-regions by the distributions of the EGE-scores in 2011 year is presented. The comparison analysis of these results with the results in 2010 year is made. The attempt to establish the factors that explain the variation of the region's distributions is made. The multiple linear regression for the average score under these factors is build.
The article examines the approaches of OLAP-applications for business analysis trucking company. Examples of using multi-dimensional tables to support decision-making.