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MODELING MARGINAL FACTORS OF CREDIT INSTITUTIONS: ELIMINATING A COLLINEARITY PROBLEM

E.Kadochnikova, S.Savdur, L. Sungatullina, E.Khisamova

First published: 2017https://doi.org/10.5593/sgemsocial2017/13/S03.059View metrics

Abstract

The article seeks to identify the most important predictors of profit of credit institutions. This involves the use of multiple regression for econometric modeling of several variables on the results of banksВ’ operations. The study aims to show the capabilities of multi-factor regression in the analysis of credit institutions. The paper proposes a methodological approach to the measurement of marginal predictors of credit institutions. The authors use special techniques to adjust the models to avoid a collinearity problem: ridge regression, principal components method. Using Excel, Gretl software the authors seek to prove the predominant influence of bank stock and private deposits on the revenue dynamics of credit institutions. The article provides a comprehensive understanding of profit formation in credit institutions. The study aims to demonstrate the negative role of the debt load of households in the banking market. Empirical assessments have confirmed the feasibility of their practical use in the management of credit institutions.

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Title
MODELING MARGINAL FACTORS OF CREDIT INSTITUTIONS: ELIMINATING A COLLINEARITY PROBLEM
Authors
E.Kadochnikova, S.Savdur, L. Sungatullina, E.Khisamova
Proceedings
4th International Multidisciplinary Scientific Conference on Social Sciences and Arts SGEM 2017
Publisher
STEF92 Technology
Year
2017
Pages
471-478
SWS Citekey
Kadochnikova20173471478
ISSN
2367-5659
ISBN
978-619-7408-15-7
Language
en
Publication type
Proceedings Paper
Keywords
credit organizationsprofitridge regressionprincipal components methoda collinearity problem
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