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Resonance phenomena in financial markets

Semenov V.P. Doctor of Economic Sciences, professor, Russian Economic University (REU) named afterPlekhanov ( emf@rea.ru )

Solov'ev U.P. Doctor of Economics, professor, Russian Economic University (REU) named afterPlekhanov ( emf@rea.ru )

Journal: Finance and Credit, #41, 2011

The model of financial market, based on the mechanistic analogy is given in the concepts of econophysics. The possibility of connecting model parameters with identifications and methods used in technical analysis in its application to mark the real state of stock market are discussed in this article. It is vividly showed in the article that financial market can be dealt as an information resonator, separating some information harmonic out of the whole stream of information’s. In this connection it is necessary to underline that the coincidence in the internal frequency of this harmonics with the internal market oscillation leads to the cost raised on some assets which actually very often can be treated as the false signals applied by the stock market to the real economy.


Methods of fuzzy set theory in credit scoring

Volkova E.S. Financial University under Government of Russian Federation, Moscow, Russian Federation ( EVolkova@fa.ru )

Gisin V.B. Financial University under Government of Russian Federation, Moscow, Russian Federation ( VGisin@fa.ru )

Solov'ev V.I. Financial University under Government of Russian Federation, Moscow, Russian Federation ( VSoloviev@fa.ru )

Journal: Finance and Credit, #35, 2017

Importance This article provides an overview of the current state of research related to the application of fuzzy set theory and fuzzy logic in credit scoring.
Objectives The article aims to describe and classify fuzzy set theory and fuzzy logic methods used in modern credit scoring models.
Methods To perform the tasks, we have studied relevant scientific publications on the article subject presented in Google Scholar.
Results The article presents a description and analysis of the basic methods of fuzzy set theory used in credit scoring.
Conclusions and Relevance The application of fuzzy sets and fuzzy logic in the models of credit scoring allows for flexible models that allow for a natural and comprehensible interpretation. The most promising direction is the use of fuzzy inference systems.


Data mining techniques: Modern approaches to application in credit scoring

Volkova E.S. Financial University under Government of Russian Federation, Moscow, Russian Federation ( EVolkova@fa.ru )

Gisin V.B. Financial University under Government of Russian Federation, Moscow, Russian Federation ( VGisin@fa.ru )

Solov'ev V.I. Financial University under Government of Russian Federation, Moscow, Russian Federation ( VSoloviev@fa.ru )

Journal: Finance and Credit, #34, 2017

Importance This article examines the current state of research in machine learning and data mining, which computational methods get combined with conventional lending models such as scoring, for instance.
Objectives The article aims to classify the modern methods of credit scoring and describe models for comparing the effectiveness of the various methods of credit scoring.
Methods To perform the tasks, we have studied relevant scientific publications on the article subject presented in Google Scholar.
Results The article presents a classification of modern data mining techniques used in credit scoring.
Conclusions Credit scoring models using machine learning procedures and hybrid models using combined methods can provide the required level of efficiency in the modern environment.


Data Mining Techniques: Modern Approaches to Application in Credit Scoring

Volkova V.S. Financial University under Government of Russian Federation, Moscow, Russian Federation ( EVolkova@fa.ru )

Gisin V.B. Financial University under Government of Russian Federation, Moscow, Russian Federation ( VGisin@fa.ru )

Solov'ev V.I. Financial University under Government of Russian Federation, Moscow, Russian Federation ( VSoloviev@fa.ru )

Journal: Digest Finance, #4, 2017

Importance This article examines the current state of research in machine learning and data mining, which computational methods get combined with conventional lending models such as scoring, for instance.
Objectives The article aims to classify the modern methods of credit scoring and describe models for comparing the effectiveness of the various methods of credit scoring.
Methods To perform the tasks, we have studied relevant scientific publications on the article subject presented in Google Scholar.
Results The article presents a classification of modern data mining techniques used in credit scoring.
Conclusions and Relevance Credit scoring models using machine learning procedures and hybrid models using combined methods can provide the required level of efficiency in the modern environment.


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