Dynamic framework for strategic forecasting of the bank consumer loan market: Evidence from Ukraine
-
Author(s)Andrii KaminskyiLink to ORCID Index: https://orcid.org/0000-0002-6574-8138
,
Nataliia VersalLink to ORCID Index: https://orcid.org/0000-0002-8801-6351
,
Oleksii PetrovskyiLink to ORCID Index: https://orcid.org/0000-0003-0254-5752
,
Nataliia PrykaziukLink to ORCID Index: https://orcid.org/0000-0002-7813-8590
-
DOIhttp://dx.doi.org/10.21511/bbs.18(3).2023.08
-
Article InfoVolume 18 2023, Issue #3, pp. 87-100
- Cited by
- 397 Views
-
160 Downloads
This work is licensed under a
Creative Commons Attribution 4.0 International License
Accurate forecasting of consumer loan market behavior gives banks a huge potential to optimize their credit strategies by proactively adapting to external changes. This study aims to analyze and predict consumer loan demand, supply, and profitability in the Ukrainian banking sector. Using a systemic dynamic approach, the interplay of five key factors is considered: central bank policies, GDP fluctuations, changing competitive landscape driven by FinTech companies, investment in government bonds as an alternative to loan granting, and severity of credit risk management.
The developed dynamic model for the bank consumer loan market in Ukraine offers predictive capabilities enhancing decision-making and strategic planning in the banking sector and can be adapted in open small economies. Within the proposed systemic dynamic model, five scenarios were explored. Compared to the base scenario, a 4 p.p. increase in the key policy rate results in UAH 4.7 billion decrease in demand for bank consumer loans and a UAH 0.55 billion reduction in lending profitability based on the year’s results. Fall in GDP by 6 p.p. leads to a decrease in the supply of bank consumer loans by UAH 6.9 billion and a decrease in lending income by UAH 1.3 billion based on the year’s results. Scenario with the decline of FinTech portfolio by 20 p.p. quarterly leads to an increase in demand for bank consumer loans of UAH 8 billion. A 4 p.p. rise in government bond yields leads to a UAH 17 billion reduction in the supply of consumer loans in the same quarter.
- Keywords
-
JEL Classification (Paper profile tab)G21, E44, E47
-
References53
-
Tables3
-
Figures10
-
- Figure 1. Causal loop diagram of MBCL
- Figure 2. Stock and flow SD diagram of the model
- Figure 3. Consumer loan portfolio outstanding: comparison of historical data and simulation within the SD model
- Figure 4. Portfolio of government bonds: comparison of historical data and simulation within the SD model
- Figure 5. Equity capital: comparison of historical data and simulation within the SD model
- Figure 6. Reserves/write-offs: comparison of historical data and simulation within the SD model
- Figure 7. Scenario analysis demand for new bank consumer loans
- Figure 8. Scenario analysis of supply of new consumer loans by banks
- Figure 9. Scenario analysis of consumer loan portfolio outstanding
- Figure 10. Scenario analysis of profit from bank consumer lending
-
- Table 1. The impact of external factors on model parameters: in total
- Table 2. Equations of the main variables of the model
- Table 3. Data for scenario analysis
-
- Abuka, C., Alinda, R. K., Minoiu, C., Peydró, J. L., & Presbitero, A. F. (2019). Monetary policy and bank lending in developing countries: Loan applications, rates, and real effects. Journal of Development Economics, 139, 185-202.
- Aikman, D., Bush, O., & Taylor, A. (2018). Monetary versus macroprudential policies: causal impacts of interest rates and credit controls in the era of the UK Radcliffe Report (NBER Working Paper No. 22380).
- Beck, R., Jakubik, P., & Piloiu, A. (2013). Non-performing loans What matters in addition to the economic cycle? (ECB Working Paper No. 1515). European Central Bank.
- Beck, T., Berrak, B., Rioja, K., & Valev, N. (2012). Who Gets the Credit? And Does It Matter? Household vs. Firm Lending across Countries. Journal of Macroeconomics, 12(1), 1-46.
- Board of Governors of the Federal Reserve System. (2023). Consumer credit.
- Bossel, H. (2007). Systems and Models: Complexity, Dynamics, Evolution, Sustainability. Books on Demand.
- Bouis, R. (2019). Banks’ Holdings of Government Securities and Credit to the Private Sector in Emerging Market and Developing Economies (IMF Working Paper. No WP/19/224).
- Brasliņš, G., Orlovs, A., Braukša, I., & Bulis, A. (2022). GDP and lending behaviour: empirical evidence for Baltic states economies. Regional Formation and Development Studies, 10(2), 31-45.
- Breeden, J., & Canals-Cerdá, J. (2018). Consumer risk appetite, the credit cycle and the housing bubble. The Journal of Credit Risk, 14(2), 45-74.
- Buchak, G., Matvos, G., Pikorski, T., & Seru, A. (2017). FinTech, regulatory arbitrage, and the rise of shadow banking (Research Paper No. 17-39). Columbia Business School.
- Calza, A., Gartner, C., & Sousa, J. M. (2001). Modelling the Demand for Loans to the Private Sector in the Euro Area. Applied Economics, 35(1), 107-117.
- Coletta, M., De Bonis, R., & Piermattei, S. (2014). The determinants of household debt: a cross-country analysis (Temi di Discussione Working Paper No. 989). Bank of Italy.
- Cornelli, G., Frost, J., Gambacorta, L., Rau, P. R., Wardrop, R., & Ziegler, T. (2023). Fintech and big tech credit: Drivers of the growth of digital lending. Journal of Banking & Finance, 148, 106742.
- Diette, M. D. (2000). How do lenders set interest rates on loans? Federal Reserve Bank of Minneapolis.
- Durkin, T., Elliehausen, G., Staten, M., & Zywicki, T. (Eds.). (2014). Introduction and Overview of Consumer Credit: Development, Uses, Kinds, and Policy Issues. In Consumer Credit and the American Economy (pp. 1-3). Oxford University Press.
- European Central Bank (ECB). (2023). Loans to households and non-financial corporations.
- FICO. (2019). Homecourt Disadvantage: Truncation Bias and the Art of Comparing Consumer Credit Scoring Models.
- Forrester, J. W. (2003). Economic theory for the new millennium. System Dynamics Review, 29(1), 26-41.
- Gennaioli, N., Martin, A., & Rossi, S. (2018). Banks, government Bonds, and Default: What do the data Say? Journal of Monetary Economics, 98, 98-113.
- Hahn, L. A., & Hagemann, H. (2015). Economic Theory of Bank Credit. Oxford University Press.
- Harvard Business Review Analytic Service. (2019). In the Game: Traditional Financial Institutions Embrace Fintech Disruption.
- Hill, J. (2018). Fintech and the Remaking of Financial Institutions. Academic Press.
- Holló, D. (2010). Estimating Price Elasticities on the Hungarian Consumer Lending and Deposit Markets: Demand Effects and Their Possible Consequences. Focus on European Economic Integration, 1, 73-89.
- Hong Kong Institute of Bankers (HKIB). (2012). Bank Lending. Wiley.
- Hughes, J. P., Jagtiani, J., & Moon, C. G. (2022). Consumer lending efficiency: commercial banks versus a fintech lender. Financial Innovation, 8, 38.
- Hunt, С. (2015). Economic implications of high and rising household indebtedness. Reserve Bank of New Zealand Bulletin, 78(1).
- IMF. (2017). Chapter 2: Household Debt and Financial Stability. In Global Financial Stability Report, October 2017: Is Growth at Risk? Monetary and Financial Systems Dept.
- Ishtiaq, M. (2015). Risk Management in Banks: Determination of Practices and Relationship with Performance (Ph.D. Thesis). University of Bedfordshire, Bedfordshire.
- Kaminskyi, A., & Petrovskyi, O. (2019). Consumer Lending in Banks: System Dynamics Modelling. Scientific Papers NaUKMA. Economics, 4(1), 48-53.
- Kaminskyi, A., & Versal, N. (2018). Risk Management of Dollarization in Banking: Case of Post-Soviet Countries. Montenegrin Journal of Economics, 14(2), 21-40.
- Kashif, M., Iftikhar, S. F., & Iftikhar, K. (2016). Loan growth and bank solvency: evidence from the Pakistani banking sector. Financial Innovation, 2(1), 22.
- Klein, N. (2013). Non-Performing Loans in CESEE: Determinants and Impact on Macroeconomic Performance (IMF Working Paper No. WP/13/72).
- Kondova, G. & Bandyopadhyay, T. (2019). The Impact of Non-Bank Lending on Bank Efficiency: Data Envelopment Analysis of European Banks. International Journal of Trade, Economics and Finance, 10(5), 108-112.
- Kupčinskas, K., & Paškevičius, A. (2017). Key factors of non-performing loans in Baltic and Scandinavian countries: lessons learned in the last decade. Ekonomika, 96(2), 43-55.
- Laryea, E., Ntow-Gyamfi, M., & Alu, A. A. (2016). Nonperforming loans and bank profitability: evidence from an emerging market. African Journal of Economic and Management Studies, 7(4), 462-481.
- Luhmann, N. (2012). Introduction to Systems Theory. Wiley.
- Lukianenko, I., & Faryna, O. (2016). Macrofinancial stability: models and assessment methods. (In Ukrainian).
- Michlitsch, K. (2020). Fintech Continues to Disrupt Consumer Lending. Morgan Stanley Investment Management.
- Minsky, H. (1986). Stabilizing an Unstable Economy. Yale University Press, New Haven, Connecticut.
- Mishra, P., Montiel, P., Pedroni, P., & Spilimbergo, A. (2014). Monetary policy and bank lending rates in low-income countries: Heterogeneous panel estimates. Journal of Development Economics, 111, 117-131.
- National Bank of Ukraine (NBU). (2023). Financial Sector Statistics.
- Onyiriuba, L. (2016). Emerging Market Bank Lending and Credit Risk Control: Evolving Strategies to Mitigate Credit Risk, Optimize Lending Portfolios, and Check Delinquent Loans. Academic Press.
- Phan, D. H. B., Narayan, P. K., Rahman, R. E., & Hutabarat, A. R. (2020). Do financial technology firms influence bank performance? Pacific-Basin Finance Journal, 62, 101210.
- Radzicki, M. J. (1990). Methodologia oeconomiae et systematis dynamis. System Dynamics Review, 6(2), 123-147.
- Radzicki, M. J. (2009). System Dynamics and Its Contribution to Economics and Economic Modeling. In Robert A. Meyer (Ed.), Complex Systems in Finance and Econometrics (pp. 727-737).
- Rinaldi, L., & Sanchis-Arellano, A. (2006). Household debt sustainability: what explains household non-performing loans? An empirical analysis (ECB Working Paper No. 570).
- Stein, R. M. (2005). The relationship between default prediction and lending profits: Integrating ROC analysis and loan pricing. Journal of Banking & Finance, 29(5), 1213-1236.
- Sterman, J. D. (2001). System Dynamics Modeling: Tools for Learning in a Complex World. California Management Review, 43(4), 8-25.
- Sterman, J. D. (2002). Business Dynamics: System Thinking and Modeling for a Complex World. Massachusetts Institute of Technology: Cambridge, MA, USA.
- Szarowska, I. (2018). Effect of macroeconomic determinants on non-performing loans in Central and Eastern European countries. International Journal of Monetary Economics and Finance, 11(1), 20.
- Versal, N., Erastov, E., Balytska, M., & Honchar, I. (2022). Digitalization Index: Case for Banking System. Statistika: Statistics and Economy Journal, 102(4), 426-443.
- Wheat, D. (2007). The feedback method. A system dynamics approach to teaching macroeconomics (Doctoral Thesis). University of Bergen, Bergen, Norway.
- Zheng, C., Bhowmik, P. K., & Sarker, N. (2019). Industry-Specific and Macroeconomic Determinants of Non-Performing Loans: A Comparative Analysis of ARDL and VECM. Sustainability, 12(1), 325.
-
Impact of the COVID-19 pandemic on bank efficiency in Vietnam
Nguyen Minh Sang
doi: http://dx.doi.org/10.21511/bbs.17(1).2022.02
Banks and Bank Systems Volume 17, 2022 Issue #1 pp. 13-23 Views: 2005 Downloads: 1057 TO CITE АНОТАЦІЯBanking system is an important part of the financial system of each country. The operating efficiency in the commercial banking system will provide the tools and financial products more attractive and relevant to the needs of actors in the economy. The purpose of this paper is to analyze the impact of COVID-19 on the efficiency of 26 Vietnamese commercial banks. The paper uses a quantitative method with the nonparametric frontier analysis, data envelopment analysis (DEA) approach, to measure the efficiency of Vietnamese commercial banks. The paper adopts an intermediation approach as the banks are viewed as financial intermediaries providing financial services and payment services to entities in the economy. Research findings reveal that Vietnamese commercial banks have effectively leveraged the positive impacts of the COVID-19 pandemic, since the average efficiency in 2020 improved over the pre-pandemic period in 2019 on the same models for comparison and estimation. Based on such findings, the study makes some suggestions and recommendations to help Vietnamese commercial banks increase their operational efficiency in the context of the prolonged pandemic.
Acknowledgment
The author wishes to acknowledge support from the Banking University – Ho Chi Minh City. This research was made possible thanks to all valuable support from relevant stakeholders. -
Competition, bank fragility, and financial crisis
Dewi Hanggraeni
doi: http://dx.doi.org/10.21511/bbs.13(1).2018.03
Banks and Bank Systems Volume 13, 2018 Issue #1 pp. 22-36 Views: 1722 Downloads: 436 TO CITE АНОТАЦІЯThis paper examines how competition affects bank fragility and how this relation varies in normal times and during a financial crisis using the data from Indonesian commercial banking industry. The author finds significant evidence, both statistically and economically, that more competition reduces bank fragility. In particular, the author finds that a decrease in Herfindahl-Hirschman Index (HHI) of deposits by 100 points leads to an increase in bank Z-score by 14.22 percent from its mean. Similarly, a decrease in HHI of loans by 100 points leads to an increase in Z36 by 20.44 percent. This finding is consistent across different kinds of robustness tests, including endogeneity, as well as alternative bank fragility and competition measures. However, this competition-stability nexus holds only in normal times and is reversed during a financial crisis. This suggests that the impact of competition on bank fragility is conditional on the economic condition.
-
Bank-specific vs. macro-economic factors: what drives profitability of commercial banks in Saudi Arabia
Farkhanda Shamim ,
Bora Aktan ,
Mohammed Attaitalla Abdulla ,
Nabeel Mohammed Yaseen Sakhi
doi: http://dx.doi.org/10.21511/bbs.13(1).2018.13
Banks and Bank Systems Volume 13, 2018 Issue #1 pp. 139-149 Views: 1662 Downloads: 250 TO CITE АНОТАЦІЯThe goal of this study is to determine the elements that contribute to the profitability of commercial banks in Saudi Arabia. The study is important due to the fact that Saudi vision 2030 foresees Saudi Arabia as a global investment powerhouse and fulfilling this objective requires a profitable banking sector. The method chosen for the study is multiple regression analysis. The sample data is taken for the period ranging 2009 and 2015 for the 12 local banks. The research concludes that bank’s internal factors specifically, bank size, liquidity, credit risk and operational efficiency are significantly determining the profitability in the banks as compared to the economy’s macro-economic variables.
