Optimal allocation of bank resources and risk reduction through portfolio decentralization

Arezoo Mohammadi (), Mehrzad Minnoei (), Zadollah Fathi (), Mohamamd Ali Keramati () and Hossein Baktiari ()
Additional contact information
Arezoo Mohammadi: IAU
Mehrzad Minnoei: Department of Industrial Management, Central Tehran Branch, Islamic Azad University
Zadollah Fathi: Department of Industrial Management, Central Tehran Branch, Islamic Azad University
Mohamamd Ali Keramati: Department of Industrial Management, Central Tehran Branch, Islamic Azad University
Hossein Baktiari: 5Department of Industrial Management, Faculty of Islamic Studies and Management, Imam Sadegh University, Tehran

International Journal of Economic Sciences, 2022, vol. 11, issue 2, 92-143

Abstract: The main concern of all economic companies is the resources equipping and allocating them in different economic sectors with the aim of maximizing profit and minimizing risk. Decentralization is one of the important factors that reduce investment risk. The investors plan to create investment by carefully planning and collecting sufficient information on the economic situation and analyzing the situation of various industries. As an economic enterprise, banks are looking for short- and long-term investments in a types of loans ,such as bailment of a capital , civil participation, reward, etc, which guarantees the return of their capital. In this paper, considering the condition of a bank as an economic enterprise, a model is presented which not only increases profit but also reduces risk. Two objective functions have been defined that the first objective is to minimize the risk and the second objective function is to maximize the of the bank profit, which is used by robust programming and Malvi Sim model. In this paper, we have investigated the Risky and non-Risky Partfolio and the optimal portfolio of bank assets from scenario based solution of the model and by using PSO and Genetic Optimization Algorithm. At all levels of confidence and optimal values of risk based on the estimation of SPP-CVAR method by Particle Swarm Algorithm (PSA) is less than genetic algorithm, which indicates better performance of Particle Swarm Algorithm (PSA) than Genetic Algorithm (GA). Also, the optimum wealth obtained from PSA solution is higher at all levels of confidence than the corresponding value of Genetic Algorithm (GA), and this is another reason to confirm the performance of PSO algorithm compared to the Genetic Algorithm (GA). The values of the first goal function, obtained from the PSO algorithm, for all confidence levels are lower than those of the genetic algorithm. The optimum wealth obtained from PSA is higher than genetic algorithm. At 0.9 level, the value of LR of kupiec statistics for the SPP-CVAR method was less than the Chi-square statistics (Critical value) which was assumed to be acceptable.

Keywords: Risky and non-risky assets; New portfolio; Bank deposits; Risk; PSO; PSA (search for similar items in EconPapers)
JEL-codes: G11 G20 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://eurrec.org/RePec/aop/jijoes/0082ES.rdf117006
https://eurrec.org/RePec/aop/jijoes/0082ES.rdf117006?download=7

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aop:jijoes:v:11:y:2022:i:2:p:92-143

Access Statistics for this article

International Journal of Economic Sciences is currently edited by Jan Cadil

More articles in International Journal of Economic Sciences from European Research Center
Bibliographic data for series maintained by Jiri Rotschedl ().