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In this chapter, we investigate the separation power of several machine learning techniques and compared them with the benchmark logistic regression using real data from 17,520 private individuals of a Romanian commercial bank. In order to capture the financial crisis effect we equally divided the data in two samples prior and posterior the crisis and we compared 13 models in terms of misclassification Type I and II Errors. As the models aim to catch best the patterns in the “default” profile of a consumer credit borrower, we split the variables in socio-demographic factors (Social Rating) and financial factors (Financial rating) and conclude that “default” profile prior crisis is captured better by the linear models while the patterns of the financial crisis are captured better by the non-linear models. We found that accuracy ratio gives the better results on decision trees and ensembles based on decision trees such as adaptive boosting methods (Financial Rating) and Random Forest (Credit Rating, Social Rating) irrespective of the sample choice. The power of the model to classify the debtors using Social Rating, Financial Rating and the mix of these, the Credit Rating, depends on the trained data used. The Financial Rating’s champion model’s results are best on posterior crisis data, meaning that financial factors counted the most in detecting the patterns in “default” after the financial crisis. The order is not the same for Social Rating, where the best classification is obtained on prior crisis data meaning that classification considering the individual’s creditworthiness is more difficult on posterior crisis “default” patterns.
Ana Maria Sandica; Monica Dudian. Determinants of Consumer Credit Default in Romania: A Comparison of Machine Learning Algorithms. Social Credit Rating 2020, 657 -674.
AMA StyleAna Maria Sandica, Monica Dudian. Determinants of Consumer Credit Default in Romania: A Comparison of Machine Learning Algorithms. Social Credit Rating. 2020; ():657-674.
Chicago/Turabian StyleAna Maria Sandica; Monica Dudian. 2020. "Determinants of Consumer Credit Default in Romania: A Comparison of Machine Learning Algorithms." Social Credit Rating , no. : 657-674.
EU countries to measure human development incorporating the ambient PM2.5 concentration effect. Using a principal component analysis, we extract the information for 2010 and 2015 using the Real GDP/capita, the life expectancy at birth, tertiary educational attainment, ambient PM2.5 concentration, and the death rate due to exposure to ambient PM2.5 concentration for 29 European countries. This paper has two main results: it gives an overview about the relationship between human development and ambient PM2.5 concentration, and second, it provides a new quantitative measure, PHDI, which reshapes the concept of human development and the exposure to ambient PM2.5 concentration. Using rating classes, we defined thresholds for both HDI and PHDI values to group the countries in four categories. When comparing the migration matrix from 2010 to 2015 for HDI values, some countries improved the development indicator (Romania, Poland, Malta, Estonia, Cyprus), while no downgrades were observed. When comparing the transition matrix using the newly developed indicator, PHDI, the upgrades observed were for Denmark and Estonia, while some countries like Spain and Italy moved to a lower rating class due to ambient PM2.5 concentration.
Ana-Maria Săndică; Monica Dudian; Aurelia Ştefănescu. Air Pollution and Human Development in Europe: A New Index Using Principal Component Analysis. Sustainability 2018, 10, 312 .
AMA StyleAna-Maria Săndică, Monica Dudian, Aurelia Ştefănescu. Air Pollution and Human Development in Europe: A New Index Using Principal Component Analysis. Sustainability. 2018; 10 (2):312.
Chicago/Turabian StyleAna-Maria Săndică; Monica Dudian; Aurelia Ştefănescu. 2018. "Air Pollution and Human Development in Europe: A New Index Using Principal Component Analysis." Sustainability 10, no. 2: 312.
The emerging economies that do not face fiscal, monetary and foreign debt pressures can use the savings generated by lower oil prices for investments in order to generate economic growth. Hence, there is no doubt that the oil price affects the economy’s resilience to shocks. The importance of this impact derives from the magnitude of the price change and its diffusion within the economy. Moreover, the sustainability of any company and of the economy as a whole is subject to the availability and the price of the energy resources. The cost of these resources is an important variable used in the majority of the models regarding the assessment of sustainable development. Therefore, this article examines the impact of the oil price changes on industrial production in Romania. We found that, similar to other countries, in Romania, the growth rate of industrial production responds more strongly to a rise in oil prices. Thus, the oil Brent price has an asymmetric effect on the production evolution. This finding suggests that macroeconomic stabilization is more difficult to achieve when the oil price rises.
Monica Dudian; Mihaela Mosora; Cosmin Mosora; Stefanija Birova. Oil Price and Economic Resilience. Romania’s Case. Sustainability 2017, 9, 273 .
AMA StyleMonica Dudian, Mihaela Mosora, Cosmin Mosora, Stefanija Birova. Oil Price and Economic Resilience. Romania’s Case. Sustainability. 2017; 9 (2):273.
Chicago/Turabian StyleMonica Dudian; Mihaela Mosora; Cosmin Mosora; Stefanija Birova. 2017. "Oil Price and Economic Resilience. Romania’s Case." Sustainability 9, no. 2: 273.