Factors influencing absorption of the banks’ financial resources: assessment of the optimality

Economic Annals-ХХI: Volume 194, Issue (11-12), Pages: 111-118

Citation information:
Stryabkova, E., Chistnikova, I., Antonova, M., Druzhnikovа, E., & Mochalova, Y. (2021). Factors influencing absorption of the banks’ financial resources: assessment of the optimality. Economic Annals-XXI, 194(11-12), 111-118. doi: https://doi.org/10.21003/ea.V194-14

Elena Stryabkova
D.Sc. (Economics),
Professor,
Head, Department of Applied Economics and Economic Security,
Belgorod National Research University
85 Pobedy Str., Belgorod, 308000, Russian Federation
stryabkova@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0002-6067-1434

Irina Chistnikova
PhD (Economics),
Associate Professor of the
Department of Applied Economics and Economic Security,
Belgorod National Research University
85 Pobedy Str., Belgorod, 308000, Russian Federation
chistnikova@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0002-9653-9929

Marina Antonova
PhD (Economics),
Associate Professor of the
Department of Applied Economics and Economic Security,
Belgorod National Research University
85 Pobedy Str., Belgorod, 308000, Russian Federation
antonova_m@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0001-7106-5352

Elena Druzhnikovа
PhD (Economics),
Associate Professor of the
Department of Applied Economics and Economic Security,
Belgorod National Research University
85 Pobedy Str., Belgorod, 308000, Russian Federation
druzhnikova@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0002-5088-6386

Yana Veklich Mochalova
PhD (Economics),
Associate Professor of the
Department of Applied Economics and Economic Security,
Belgorod National Research University
85 Pobedy Str., Belgorod, 308000, Russian Federation
leschinskaya@bsu.edu.ru
ORCID ID: https://orcid.org/0000-0001-8385-9328

Factors influencing absorption of the banks’ financial resources: assessment of the optimality

Abstract. Optimal allocation of deposits (resources) and facilities (expenditures) are among the most important strategies of banks. Optimal allocation of resources in various economic sectors leads to targeting of funds collected by banks. Optimal allocation of expenses, in addition to obtaining returns in excess of the cost of attracting resources, leads to rapid and timely response to bank customers.
The purpose of this study is to provide linear programming models to determine the optimal combination of resources and expenditures of banks with the approach of reducing the cost of money. The tools used in this research are linear programming models and the data used is quantitative. The results of this study showed that all five factors (electronic banking, physical factors and conditions, service factors, communication and human factors and financial factors, respectively) were effective factors in attracting resources. They are influential in the bank, respectively.

Keywords: Banking; Banking Resources; Customers; Attracting Banking Resources; Banking Resources Portfolio

JEL Classifications: A10; A20; B00; D40

Acknowledgements and Funding: The authors received no direct funding for this research.

Contribution: The authors contributed equally to this work.

Data Availability Statement: The dataset is available from the authors upon request.

DOI: https://doi.org/10.21003/ea.V194-14

References

1. Antonova, M. V., & Polyanskaya, M. A. (2016). Organizational model of stress testing to assess the financial stability of a Bank. Herald of the Belgorod University of Cooperation, Economics and Law, 60(4), 181-189 (in Russ.).
2. Bolshakov, D. V., & Polskikh, A. Yu. (2016). The concept of choice as an element of decision theory. Society and power, 57(1), 97-100.
3. Brunnermeier, M. K., & Niepelt, D. (2019). On the equivalence of private and public money. Journal of Monetary Economics, 106, 27-41.
   doi: https://doi.org/10.1016/j.jmoneco.2019.07.004
4. Chin, M. K., Zhang, S. X., Jahanshahi, A. A., & Nadkarni, S. (2021). Unpacking political ideology: CEO social and economic ideologies, strategic decision-making processes, and corporate entrepreneurship. Academy of Management Journal, 64(4), 1213-1235.
   doi: https://doi.org/10.5465/amj.2019.1228
5. Cho, T.-Y., & Chen, Y.-Sh. (2021). The impact of financial technology on China’s banking industry: An application of the metafrontier cost Malmquist productivity index. The North American Journal of Economics and Finance, 57, 101414.
   doi: https://doi.org/10.1016/j.najef.2021.101414
6. Ferrante, F. (2019). Risky lending, bank leverage and unconventional monetary policy, Journal of Monetary Economics, 101. 100-127.
   doi: https://doi.org/10.1016/j.jmoneco.2018.07.014
7. Garayeva, E. A., & Mansimov, K. B. (2017). A necessary condition for optimality in a discrete-time control problem with an undifferentiated quality criterion. Bulletin of Tomsk state University. Management, computer engineering and Informatics, 38, 4-10 (in Russ.).
8. García Cabello, J., & Lobillo, F. J. (2017). Sound branch cash management for less: A low-cost forecasting algorithm under uncertain demand, 70, 118-134.
   doi: https://doi.org/10.1016/j.omega.2016.09.005
9. Gong, Z., Tan, X., & Yang, Y. (2019). Optimal weighting models based on linear uncertain constraints in intuitionistic fuzzy preference relations. Journal of the Operational Research Society, 70(8), 1296-1307.
   doi: https://doi.org/10.1080/01605682.2018.1489349
10. Hafezalkotob, A., Hafezalkotob, A., Liao, H., & Herrera, F. (2019). An overview of MULTIMOORA for multi-criteria decision-making: Theory, developments, applications, and challenges. Information Fusion, 51, 145-177.
    doi: https://doi.org/10.1016/j.inffus.2018.12.002
11. Mbanyele, W. (2020). Do busy directors impede or spur bank performance and bank risks? Event study evidence from Brazil. SAGE Open, 10(2), 2158244020933594.
    doi: https://doi.org/10.1177%2F2158244020933594
12. Osipov, G. V., Nifontov, V. A., & Domrachev, D. V. (2016). Methodological problems of the theory of state decision-making in the context of integrative competence approach. Scientific review. Series 2: arts and Humanities, 1, 3-10 (in Russ.).
13. Petchrompo, S., Wannakrairot, A., & Parlikad, A. K. (2022). Pruning pareto optimal solutions for multi-objective portfolio asset management. European Journal of Operational Research, 297(1), 203-220.
    doi: https://doi.org/10.1016/j.ejor.2021.04.053
14. Prakash, N., Singh, Sh., & Sharma, S. (2021). Technological diffusion, banking efficiency and Solow’s paradox: A frontier-based parametric and non-parametric analysis, Structural Change and Economic Dynamics, 58, 534-551.
    doi: https://doi.org/10.1016/j.strueco.2021.07.007
15. Sukhanova, A. G. (2017). Three-criteria optimization of the securities portfolio using fuzzy set theory in the MATHCAD system. Computer mathematics systems and their applications, 18, 37-41 (in Russ.).
16. Ting, I. W. K., Tebourbi, I., Lu, W. M., & Kweh, Q. L. (2021). The effects of managerial ability on firm performance and the mediating role of capital structure: evidence from Taiwan. Financial Innovation, 89(7).
    doi: https://doi.org/10.1186/s40854-021-00320-7
17. Tsyplakov, O. N., Poldo, V. I., & Golovin, Y. U. (2015). The importance of probability theory in making economic decisions. International student scientific Bulletin, 3-4, 488-489 (in Russ.).
18. Valle-Cruz, D., Fernandez-Cortez, V., & Gil-Garcia, J. R. (2022). From E-budgeting to smart budgeting: Exploring the potential of artificial intelligence in government decision-making for resource allocation. Government Information Quarterly, 39(2), 101644.
    doi: https://doi.org/10.1016/j.giq.2021.101644

Received 16.08.2021
Received in revised form 19.09.2021
Accepted 29.09.2021
Available online 27.12.2021