Кластеризация заемщиков - физических лиц по уровню дефолтов: рейтинговый подход (на примере регионов Сибирского федерального округа)
This article suggested method of probability estimation of defaults in the retail. This method is based on the clustering of borrowers depending on the level of credit risk and satisfying international standards of credit risk. Performed testing methodology on the example of some regions of the Siberian Federal District, identified the essential features of the behavior of borrowers in the regions. Calculated the ratings of the regions in terms of probability of defaults on consumer loans (retail).
This state-of-the-art survey is dedicated to the memory of Emmanuil Markovich Braverman (1931-1977), a pioneer in developing the machine learning theory. The 12 revised full papers and 4 short papers included in this volume were presented at the conference "Braverman Readings in Machine Learning: Key Ideas from Inception to Current State" held in Boston, MA, USA, in April 2017, commemorating the 40th anniversary of Emmanuil Braverman's decease. The papers present an overview of some of Braverman's ideas and approaches. The collection is divided in three parts. The first part bridges the past and the present. Its main contents relate to the concept of kernel function and its application to signal and image analysis as well as clustering. The second part presents a set of extensions of Braverman's work to issues of current interest both in theory and applications of machine learning. The third part includes short essays by a friend, a student, and a colleague.
The mass application of mobile cardiographs already leads to both explosive quantitative growth of the number of patients available for ECG study, registered daily outside the hospital (Big DATA in cardiology), and to the emergence of new qualitative opportunities for the study of long-term oscillatory processes (weeks, months, years) of the dynamics of the individual state of the Cardiovascular system of any patient.
The article demonstrates that new opportunities of long - term continuous monitoring of the Cardiov ascular system state of patients ' mass allow to reveal the regularities (DATA MINING) of Cardiovascular system dynamics, leading to the hypothesis of the existence of an adequate Cardiovascular system model as a distributed nonlinearself - oscillating system of the FPU recurrence model class . The presence of a meaningful mathematical model of Cardiovascular system within the framework of the FPU auto – recurrence , as a refinement of the traditional model of studying black box, further allows us to offer new computational methods for ECG analysis and prediction of Cardiovascular system dynamics for a refined diagnosis and evaluation of the effectiveness of the treatment.
The paper describes the results of an experimental study of topic models applied to the task of single-word term extraction. The experiments encompass several probabilistic and non-probabilistic topic models and demonstrate that topic information improves the quality of term extraction, as well as NMF with KL-divergence minimization is the best among the models under study.
Technology mining (TM) helps to acquire intelligence about the evolution of research and development (R&D), technologies, products, and markets for various STI areas and what is likely to emerge in the future by identifying trends. The present chapter introduces a methodology for the identification of trends through a combination of “thematic clustering” based on the co-occurrence of terms, and “dynamic term clustering” based on the correlation of their dynamics across time. In this way, it is possible to identify and distinguish four patterns in the evolution of terms, which eventually lead to (i) weak signals of future trends, as well as (ii) emerging, (iii) maturing, and (iv) declining trends. Key trends identified are then further analyzed by looking at the semantic connections between terms identified through TM. This helps to understand the context and further features of the trend. The proposed approach is demonstrated in the field photonics as an emerging technology with a number of potential application areas.
In the paper some prominent features of a modern financial system are studied using the model of leverage dynamics. Asset securitization is considered as a major factor increasing aggregate debt and hence systems uncertainty and instability. A simple macrofinancial model includes a logistic equation of leverage dynamics that reveals origins of a financial bubble, thus corresponding closely to the Minsky financial instability hypothesis. Using ROA, ROE, and the interest rate as parameters, the model provides wide spectrum of leverage and default probability trajectories for the short and long run.
This is a textbook in data analysis. Its contents are heavily influenced by the idea that data analysis should help in enhancing and augmenting knowledge of the domain as represented by the concepts and statements of relation between them. According to this view, two main pathways for data analysis are summarization, for developing and augmenting concepts, and correlation, for enhancing and establishing relations. Visualization, in this context, is a way of presenting results in a cognitively comfortable way. The term summarization is understood quite broadly here to embrace not only simple summaries like totals and means, but also more complex summaries such as the principal components of a set of features or cluster structures in a set of entities.
The material presented in this perspective makes a unique mix of subjects from the fields of statistical data analysis, data mining, and computational intelligence, which follow different systems of presentation.
The article presents a model of optimization of inventory control strategy in terms of risk in the supply chain enterprises meat industry. On study the approach to the transformation of the model under conditions of uncertainty in the model of risk management by using the method of decision tree. Based on the method of decision tree for the corresponding model in terms of risk determine the optimal strategy, which provides a different attitude to risk.
In research is carried out analysis and proposed models for assessment estimating the investment level and investment potential of companies Models are tested on the example of leading IT companies. It is suggested the ranking method for companies' market indicators regarding the investment attractiveness by information available in open sources.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.