### Analysis of bank credit portfolio and the ways of its improvement

#### Abstract

The work is devoted to investigation of the loan portfolio. In order to assess the effectiveness of the loan portfolio the methods of classical and modern theories of portfolio are used. As a basis for the analysis the portfolio of one of Ukrainian banks and quarterly data for the period from the second quarter of 2011 up to the fourth quarter of 2013 are used. All borrowers are divided into three groups according to the sum of the loan: the first group - up to 10,000 UAH, the second - from 10,000 UAH to 100,000 UAH, the third - more than 100,000 UAH. The main characteristics of the portfolio under consideration is the expected change of profit and its risk. While analyzing the risk of the portfolio three different approaches to the formation of an effective portfolio are used. They are the risk minimization, maximization of the Sharpe ratio and of the expected usefulness. The dependence of the structure of the portfolio on the attitude of banking institutions to risk is considered. It is shown according to all the criteria the available loan portfolio is completely ineffective. The directions of changes in the structure of the portfolio in order to improve its profitability and at the same time to reduce the risk are determined. Having added an additional condition to the model regards the proportion of unreturned loans, we have shown that the biggest quantity of unreturned loans is in the third group(the sum of loan is bigger than 100,000 UAH) and the lowest part of unreturned loans is in the first group(the sum of loan is not bigger than 10,000 UAH). Moreover, for loans amounting more than 100,000 UAH the level of risk is the highest while the expected income is the lowest. For loans up to $ 10,000 UAH the level of risk is the lowest while the expected income is the highest. As the strategy for loans attraction worth more than 100,000 UAH is the basis of marketing policy of the bank, we can conclude that bank policy is totally ineffective. That is, we can talk ineffective evaluation of borrowers’s solvency taking into consideration that the proportion of unreturned loans is 39.3% of the total sum of the issued credit.

#### Keywords

#### References

Grynko, O. L., Khokhlov V. V., Koryahina, G. S. (2008). The credit risk of the bank on the basis of diversification. World of Finance, 3, 99-105.

Grynko, O. L. (2011). Theoretical aspects of determining the nature and diversification of the loan portfolio. Bulletin of the National Bank, 4, 38-45.

Vitlinskyy, V. V. (2006). Credit risk as an important component of banking risks. Finance of Ukraine, 8, 86-96.

Vasyurenko, O. V. & Podchesova V. Y. (2011). Current concepts of credit risk management as key components of the bank's credit risk management. Actual problems of economy, 1,170-177.

Prymostka, L. A. (2007). Banking Risks: Theory and Practice Management. Kyiv National Economic University. Kyiv: MBK.

Hurd, T. R. (2009). Credit risk modeling using time-changed Brownian motion. International journal of theoretical and applied finance, 12, 1213-1230.

Deng, S., Giesecke K., Lai, T. L. (2012). Sequential importance sampling and resampling for dynamic portfolio credit risk. Operations research, 60 (1), 78-91.

Markowitz, H. (1952). Portfolio selection. Journal of finance, 7, 77-91.

Basel committee on banking supervision. (2001). Operational risk consultative document, supporting document to the New Basel Capital Accord. Retrieved October 07, 2014, from http://www.bis.org/publ/bcbsca02.pdf.

Alexander, G. J. & Baptista, M. A. (2002). Economic implication of using a mean-VaR model for portfolio selection: a comparison with mean-variance analysis. Journal of economic dynamics & control, 26, 1159-1193.

Bodnar, T., Schmid, W., Zabolotskyy, T. (2012). Minimum VaR and Minimum CVaR optimal portfolios: estimators, confidence regions, and tests. Statistics & Risk Modeling, 29, 281-314.

Zabolotskii, T. N., Bodnar T. D., Vitlinskyy, V. V. (2012). Portfolio choice problem with the Value-at-Risk utility function under general linear constraints. Economic Cybernetics, 4-6 (76-78), 4-11.

Sharpe, W. F. (1994). The Sharpe ratio. The journal of portfolio management, 21(1), 49-58.

Bodnar, T. D. & Zabolotskii, T. N. (2013). Sharpe ratio maximization portfolio of financial assets in the context of risk minimization. Economic Journal – XXI, 11-12 (1), 110-113.

Okhrin, Y. & Schmid, W. (2006). Distributional properties of optimal portfolio weights. Journal of econometrics, 134, 235-256.

Zabolotskyy, T. & Vitlinskyy V. (2013). The distribution of the characteristics of the maximum expected utility portfolio based on VaR: the impact of investor's risk aversion coefficient. Economic Cybernetics, 4-6 (82-84), 4-11.

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Science Works Journal "Ekonomichnyy analiz"

ISSN 1993-0259 (Print) ISSN 2219-4649 (Online) DOI: 10.35774/econa

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