IDEAS home Printed from https://ideas.repec.org/a/iaf/journl/y2019i4p78-87.html
   My bibliography  Save this article

Analysis and Forecasting of the Bank's Performance: The Case of the Privatbank

Author

Listed:
  • Tetiana Payanok

    (University of State Fiscal Service of Ukraine, Irpin, Ukraine)

  • Mariya Kamenchuk

    (University of State Fiscal Service of Ukraine, Irpin, Ukraine)

Abstract

The banking sector of the economy has a direct impact on the activities of business entities of the country. This determines the need for efficient and rational management of banks activities in order to comprehensively assess the impact of external and internal factors on the bank's profitability. In Ukraine, Privatbank is one of the leaders of the banking system. This bank has been operating transparently in the market for a long time and has the confidence of the population. Therefore this bank was chosen for the study. The purpose of the article is to establish the level of dependence of the financial results of Privatbank on the influence of internal and external factors and forecasting its financial performance indicators. The dynamics of the main financial indicators of the bank's activity over eighteen years was analyzed, their average annual growth was determined. The crisis period for the bank lasted two years, when there was a significant drop in the financial result due to the political and economic crisis in the country and the process of nationalization of the bank. During this period, the bank lost 11.8 billion. UAH of profit. Statistical analysis showed a normal distribution of assets, liabilities, equity, income and GDP, so it allows authors to make a prediction using confidence intervals. As the correlation analysis shows, the vast majority of the analyzed indicators have a strong impact on the bank's equity, except for the inflation indicator. It was determined that the Privatbank financial results are most dependent on the GDP and the size of the population’s income per year. The effect of equity and the rate of national currency on bank's profit is average. Using a box diagram, extreme emissions were analyzed, which significantly influenced the distribution of indicators. According to the results of the study, authors recommend to use the method of partial correlation to assess the relationship between factors that affect the overall indicator.

Suggested Citation

  • Tetiana Payanok & Mariya Kamenchuk, 2019. "Analysis and Forecasting of the Bank's Performance: The Case of the Privatbank," Oblik i finansi, Institute of Accounting and Finance, issue 4, pages 78-87, December.
  • Handle: RePEc:iaf:journl:y:2019:i:4:p:78-87
    as

    Download full text from publisher

    File URL: http://www.afj.org.ua/pdf/702-analiz-i-prognozuvannya-rezultativ-diyalnosti-banku-na-prikladi-pat-kb-privatbank.pdf
    Download Restriction: no

    File URL: http://www.afj.org.ua/en/article/702/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. D Rösch & H Scheule, 2014. "Forecasting probabilities of default and loss rates given default in the presence of selection," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 393-407, March.
    2. Alina Derkachenko & Yuliya Khudolii, 2018. "Analysis of Business Models of Ukrainian Banks," Oblik i finansi, Institute of Accounting and Finance, issue 2, pages 76-83, June.
    3. Oleksiy Kalivoshko, 2019. "Analysis of Systemically Important Commercial Banks," Oblik i finansi, Institute of Accounting and Finance, issue 1, pages 83-91, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Anastasios Petropoulos & Vasilis Siakoulis & Dionysios Mylonas & Aristotelis Klamargias, 2018. "A combined statistical framework for forecasting default rates of Greek Financial Institutions' credit portfolios," Working Papers 243, Bank of Greece.
    2. Jean-David Fermanian, 2020. "On the Dependence between Default Risk and Recovery Rates in Structural Models," Annals of Economics and Statistics, GENES, issue 140, pages 45-82.
    3. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2016. "Accuracy of mortgage portfolio risk forecasts during financial crises," European Journal of Operational Research, Elsevier, vol. 249(2), pages 440-456.
    4. D’Amico, Guglielmo & Gismondi, Fulvio & Petroni, Filippo & Prattico, Flavio, 2019. "Stock market daily volatility and information measures of predictability," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 518(C), pages 22-29.
    5. Krüger, Steffen & Rösch, Daniel, 2017. "Downturn LGD modeling using quantile regression," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 42-56.
    6. Florian Kaposty & Philipp Klein & Matthias Löderbusch & Andreas Pfingsten, 2022. "Loss given default in SME leasing," Review of Managerial Science, Springer, vol. 16(5), pages 1561-1597, July.
    7. Nazemi, Abdolreza & Fabozzi, Frank J., 2018. "Macroeconomic variable selection for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 14-25.
    8. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
    9. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2018. "Systematic Effects among Loss Given Defaults and their Implications on Downturn Estimation," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1113-1144.
    10. Peter-Hendrik Ingermann & Frederik Hesse & Christian Bélorgey & Andreas Pfingsten, 2016. "The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values," Business Research, Springer;German Academic Association for Business Research, vol. 9(2), pages 179-228, August.
    11. Jonathan Crook & David Edelman, 2014. "Special issue credit risk modelling," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 321-322, March.
    12. Lützenkirchen, Kristina & Rösch, Daniel & Scheule, Harald, 2014. "Asset portfolio securitizations and cyclicality of regulatory capital," European Journal of Operational Research, Elsevier, vol. 237(1), pages 289-302.
    13. Krüger, Steffen & Oehme, Toni & Rösch, Daniel & Scheule, Harald, 2018. "A copula sample selection model for predicting multi-year LGDs and Lifetime Expected Losses," Journal of Empirical Finance, Elsevier, vol. 47(C), pages 246-262.
    14. Do, Hung Xuan & Rösch, Daniel & Scheule, Harald, 2018. "Predicting loss severities for residential mortgage loans: A three-step selection approach," European Journal of Operational Research, Elsevier, vol. 270(1), pages 246-259.
    15. Alexander M. Karminsky & Ella Khromova, 2018. "Increase of banks’ credit risks forecasting power by the usage of the set of alternative models," Russian Journal of Economics, ARPHA Platform, vol. 4(2), pages 155-174, June.
    16. Nazemi, Abdolreza & Heidenreich, Konstantin & Fabozzi, Frank J., 2018. "Improving corporate bond recovery rate prediction using multi-factor support vector regressions," European Journal of Operational Research, Elsevier, vol. 271(2), pages 664-675.
    17. Ellen Tobback & David Martens & Tony Van Gestel & Bart Baesens, 2014. "Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 376-392, March.
    18. Abdelkader Derbali & Lamia Jamel, 2019. "Dependence of Default Probability and Recovery Rate in Structural Credit Risk Models: Case of Greek Banks," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 10(2), pages 711-733, June.
    19. Lee, Yongwoong & Rösch, Daniel & Scheule, Harald, 2021. "Systematic credit risk in securitised mortgage portfolios," Journal of Banking & Finance, Elsevier, vol. 122(C).
    20. Jennifer Betz & Maximilian Nagl & Daniel Rösch, 2022. "Credit line exposure at default modelling using Bayesian mixed effect quantile regression," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(4), pages 2035-2072, October.

    More about this item

    Keywords

    assets; equity; deposits; obligations; financial result; statistical analysis; forecasting the banks activities; normal distribution; correlation analysis; linear dependence;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:iaf:journl:y:2019:i:4:p:78-87. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Serhiy Ostapchuk (email available below). General contact details of provider: https://edirc.repec.org/data/iafkvua.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.