Households’ credit demand: Main trends and characteristics for Ukraine
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Author(s)Lyudmyla ShkvarchukLink to ORCID Index: https://orcid.org/0000-0001-7241-3961
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Rostyslav Slav’yukLink to ORCID Index: https://orcid.org/0000-0002-0904-8970
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DOIhttp://dx.doi.org/10.21511/bbs.16(3).2021.02
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Article InfoVolume 16 2021 , Issue #3, pp. 13-22
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Household demand for credits is quite volatile, which requires constant evaluation of it changes. The purpose of the paper is to identify quantitative signals, the use of which increases the predictability of the credit market development. The study utilizes technical analysis methods for an econometric estimation of trends in household demand for credits in Ukraine for the 2002–2019 period. Based on the analysis of historical market lows, it was argued that with all the negative effects of destabilizing factors, the household demand for loans will not fall below the market support point of UAH 50 million. The financial behavior of Ukrainian households when choosing the type of loan is stable and does not change with fluctuations in GDP. Short-term loans are quite dynamic and largely depend on macroeconomic conditions, provoking market movements. If the relevant direction is supported by medium-term loans, the general market trend will correspond to the GDP trend. The demand for long-term loans is quite inertial, its change does not affect the overall market trend. The constant and variable elements of household demand for credit are highlighted.
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JEL Classification (Paper profile tab)D14, G14, G17, G51, E32
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References31
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Tables4
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Figures2
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- Figure 1. Growth trend of the Ukrainian retail lending market for the period 2005–2019
- Figure 2. Growth trend of the retail loan market superimposed on the Fibonacci grid chart
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- Table 1. Trend correction point indicators, calculated according to the Fibonacci theory (F) and actual (A)
- Table 2. Pilot testing parameters of the chi-square of H0
- Table 3. Correlation matrix
- Table 4. Comparison of partial Fx-criteria of the regression equation
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- Alter, A., Feng, A. X., & Valckx, N. (2018). Understanding the Macro-Financial Effects of Household Debt: A Global Perspective (IMF Working Paper No. 18/76).
- Brillinger, D. R. (1995). Trend analysis: binary-valued and point cases. Stochastic Hydrology and Hydraulics, 9, 207-213.
- Cherkasova, S., Kalaitan, T., Rushchyshyn, N., Yaremko, I., & Yaroshevych, N. (2020). Stimulating and limiting factors for the growth of investment potential of Ukrainian insurance companies. Investment Management and Financial Innovations, 17(1), 85-96.
- Chudik, A., Mohaddes, K., Pesaran, M. H., & Raissi, M. (2015). Is There a Debt-threshold Effect on Output Growth? (IMF Working Paper No. WP/15/197).
- Cuzick, J. (1988). Trend tests. In Encyclopedia of Statistical Sciences (pp. 336-342). Wiley-Interscience.
- Cynamon, B. Z., & Fazzari, S. M. (2008). Household debt in the consumer age: source of growth – risk of collapse. Capitalism and Society, 3(2), 3.
- Dejuán, D. (2017). The dynamics of household debt and GDP: Reconsidering Minsky from an empirical perspective (US, 1980–2016). In XXIV Encuentro de Economía Pública (pp. 87-164). Universidad de Castilla-La Mancha.
- Du, R. Y., & Kamakura, W. A. (2012). Quantitative Trendspotting. Journal of Marketing Research, XLIX, 514-536.
- Duygan-Bump, B., & Grant, C. (2008). Household debt repayment behaviour: what role do institutions play? (Working Paper No. QAU08-3). Federal Reserve Bank of Boston.
- Eggertsson, G. B., & Krugman, P. (2012). Debt, Deleveraging, and the Liquidity Trap: A Fisher-Minsky-Koo Approach. Quarterly Journal of Economics, 127(3), 1469-1513.
- Glick, R., & Lansing, K. J. (2010). Global household leverage, house prices, and consumption. FRBSF Economic Letter,1, 1-5.
- Greben, J. M. (2007). A theory of quantitative trend analysis and its application to South African general elections. South African Journal of Science, 103(5-5), 232-238.
- Juárez, C. F. (2015). The Credit Behavior of Households – A Behavioral Approach.
- Kane, A. (2020). Secteurimmobilier et asymmetries macroéconomiques (Ph.D. Thesis). Université de Sherbrooke. (In French).
- Kannadas, S. (2021). Investment behavior of short-term versus long-term individual investors of PAN India – An empirical study. Investment Management and Financial Innovations, 18(2), 223-233.
- Kim, Y. K. (2016). Macroeconomic effects of household debt: an empirical analysis. Review of Keynesian Economics, 4(2), 127-150.
- Korinek, A., & Simsek, A. (2016). Liquidity Trap and Excessive Leverage. American Economic Review, 106(3), 699-738.
- L´opez-Salido, D., Stein, J. C., & Zakrajˇsek, E. (2017). Credit-Market Sentiment and the Business Cycle. The Quarterly Journal of Economics, 132(3), 1373-1426.
- Landau, E. (2020). The Fibonacci Sequence Is Everywhere – Even the Troubled Stock Market. Smithsonian Magazine.
- Lombardi, M., Mohanty, M., & Shim, I. (2017). The real effects of household debt in the short and long run (BIS Working Papers No. 607).
- LvGanga, Yu, B., Chen, Z., Zhou, Z., & Shi, Y. (2012). The Analysis of Peasant Household’s Credit Behaviour. Procedia Computer Science, 9, 1228-1233.
- Mian, A., & Sufi, A. (2010). Household leverage and the recession of 2007 to 2009. IMF Economic Review, 58(1), 74-117.
- Mian, S., Sufi, A., & Verner, E. (2017). Household Debt and Business Cycles Worldwide. Quarterly Journal of Economics, 132(4), 1755-1817.
- Palley, T. I. (1994). Debt, aggregate demand, and the business cycle: an analysis in the spirit of Kaldor and Minsky. Journal of Post Keynesian Economics, 16(3), 371-390.
- Pelzer, B., Eisinga, R., & Franses, P. H. (2001). Estimating transition probabilities from a time series of independent cross sections. Statistica Neerlandica, 55(2), 249-262.
- Sági, J., Chandler, N., & Lentner, C. (2020). Family businesses and predictability of financial strength: a Hungarian study. Problems and Perspectives in Management, 18(2), 476-489.
- Santos, A. C., Costa, V., & Teles, N. (2014). The Political Economy of Consumption and Household Debt: An Interdisciplinary Contribution. RCCS Annual Review A selection from the Portuguese Journal Revista Crítica de Ciências Sociais, 6.
- Shkvarchuk, L., & Slav’yuk, R. (2019). The Financial Behavior of Households in Ukraine. Journal of Competitiveness, 11(2), 144-159.
- Villez, K. (2014). Qualitative trend analysis for process monitoring and supervision based on likelihood optimization: state-of-the-art and current limitations. IFAC Proceedings Volumes, 47(3), 7140-7145.
- Zakaria, R. H., Jaafar, N., & Ishak, N. A. (2017). Household debt decision: poverty or psychology? International Journal of Business and Society, 18(3), 515-532.
- Zinman, J. (2014). Household Debt: Facts, Puzzles, Theories, and Policies (NBER Working Paper No. 20496).
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Dynamic framework for strategic forecasting of the bank consumer loan market: Evidence from Ukraine
Andrii Kaminskyi ,
Nataliia Versal ,
Oleksii Petrovskyi ,
Nataliia Prykaziuk
doi: http://dx.doi.org/10.21511/bbs.18(3).2023.08
Banks and Bank Systems Volume 18, 2023 Issue #3 pp. 87-100 Views: 381 Downloads: 154 TO CITE АНОТАЦІЯAccurate 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.
