Modeling risk in accordance with the financing structure in the money market based on probabilistic decision theory

Subject Areas : Journal of Investment Knowledge

hamidreza iravani ¹ ( Department of Accounting and Management, Faculty of Educational Sciences and Consulting (Management and Accounting), Vahedroodhan, Islamic Azad University, Roudehen, Iran )
hamidreza kordlouieuie ² ( Department of Financial Management, Faculty of Management and Accounting, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran (Corresponding Author) )
narges yazdanian ³ ( Assistant Professor at Rudehen Islamic Azad University )

Received: 2020-11-12 Accepted : 2020-11-29 Published : 2021-03-21

Keywords: Keywords: financing structure, liquidity risk, probabilistic decision theory, money market, systematic risk,

Abstract :

has not been raised in recent years, but has been the focus of researchers in recent decades. Different sources of financing make it possible to make the desired investment and can increase the wealth of shareholders. Therefore, considering the importance of risk in the financing structure, the purpose of this study is to model risk in accordance with the financing structure in the money market based on probabilistic decision theory. In terms of research method, this research is in the category of descriptive-analytical research of the time series type. The statistical population of the study is experts in the field of financial management of banks. In this study, after reviewing various literature in the fields of financial risks and financial ratios of banks, the most important risks were identified. A combination of two methods was used to collect data. By using the library method of the subject literature; A theoretical framework and background were provided for the research, and in the second stage, we modeled by collecting the opinions of experts. In this study, after collecting information, Ahp technique was used. The results showed that systematic risk has the highest priority. Liquidity risk is in the second priority. Income distribution risk in the third priority, operational risk in the fourth priority, capital risk in the fifth priority, credit risk in the sixth priority,Market risk in the seventh priority,Competition risk in the Eighth priority andMarket liquidity risk in the last priority. has it.

References:

• رهنمای رودپشتی، فریدون؛ امینی، محمدرضا؛ شمسی، حسن؛ رضایی، معصومه.(1398). طراحی شاخص ترکیبی ریسک در بانک ها-رویکرد تحلیل پوششی داده های چندلایه (مورد مطالعه: بانک های عضو بورس اوراق بهادار تهران)، قیق در عملیات در کاربردهای آن، دوره 16، شماره2، صص 113-97.

*       جلیلوند، ابوالحسن؛ رستمی نوروزآباد، مجتبی؛ عسکری فیروزجایی، احسان؛ رحمانیانی، میلاد.(1398). یازده‏ سازی مدیریت ریسک سازمانی؛ شناسایی، تحلیل و ارزیابی مورد مطالعه: نهاد مالی فعال در بازار سرمایۀ ایران،مدیریت دارایی و تامین مالی، دوره7، شماره2، صص1-24.

*    صحراکاران، مینا؛ رضایی، فرزین.(1397). تاثیر ریسک اطلاعات مالی بر رابطه نمایندگی با ساختار سرمایه شرکت ها، مدیریت دارایی و تامین مالی، دوره 6، شماره 4،صص 102-93.

• مشایخ, شهناز؛ طاهری، ماندانا .(1395). معرفی ابزار نوین تامین مالی در بازار پول ایران: صندوق های بازار پول، بیست و ششمین کنفرانس سالانه سیاست‌های پولی و ارزی، تهران، پژوهشکده پولی و بانکی.


• مهراسا، مهتاب؛ محمدی، تیمور.(1398). ارائه تئوری ارزش فرین و ارزش در معرض ریسک: کاربرد در بازار نفت ایران، پزوهشنامه اقتصاد انرژی ایران، .(DOI): 22054/jiee.2019.9991.


• Acharya, V., Engle, R., & Richardson, M. (2012). Capital shortfall: a new approach to ranking and regulating systemic risks. American Economic Review: Papers & Proceedings, 102(3), 59-64.


• Aki-Hiro,A.(2012). A method to quantify risks of financial assets:An empirical analysis of Japanese security prices, Advanced Materials Research Vols 452-453 , 469-473.


• Ashraf, D., Mohammad, N., (2014). Matching perception with the reality—Performance of Islamic equity investments. Pacific-Basin Finance Journal, 28(C), 175-189.


• Balcılar, M., Demirer, R. and Hammoudeh, S. (2015). Global risk exposures and industry diversification with Shariah-compliant equity sectors. Pacific-Basin Finance Journal, In Press.


• Bluhm, M., & Krahnen, J. (2014). Systemic risk in an interconnected banking system with endogenous asset markets. Journal of Financial Stability, 13(1), 75-94.


• Bongini, P., Nieri, L., Pelagatti, M., & Piccini, A. (2017). Curbing systemic risk in the insurance sector: A mission impossible? The British Accounting Review, 49(2), 2256-273.


• Brammertz, W., & Mendelowitz, A. I. (2014). Limits and opportunities of big data for macro-prudential modeling of financial systemic risk. International Workshop on Data Science for Macro-Modeling . 1-6.


• Brownlees, C., & Engle, R. F. (2017). Srisk: a conditional capital shortfall measure of systemic risk (Working paper No. 37). European systemic risk board.


• Caccioli,F. Barucca,P. Kobayashi,T.(2018). Network models of financial systemic risk: a review, Comput Soc Sc , 1:81–114.


• Canbolat ,M. Gümrah, A.(2015). Analysis of Credit Risk Measurement Models in the Evaluation of Credit Demands, Universal Journal of Accounting and Finance 3(1): 16-20,


• Carmassi, J., & Herring, R. (2016). The corporate complexity of global systemically important banks. Journal of Financial Services Research, 492, 175-201.


• Cerchiello, P., Giudici, P., & Nicola, D. (2016). Big data models of bank risk contagion (DEM Working Paper Series No. 117 (02-16)).

*       D’Alpaos,A. Canesi, R.(2014). MCDM Approaches in Property Investments: An AHP Model for Risk Assessment, International Journal of the Analytic Hierarchy Process, Washington D.C., U.S.A.

• Dixon,M.F. Akcora,C. G. Gel,Y.R.(2019). Blockchain analytics for intraday fnancial risk modeling, Digital Finance https://doi.org/10.1007/s42521-019-00009-8.


• Ewanchuk,L. Frei,Ch.(2019). Recent Regulation in Credit Risk Management:A Statistical Framework, Risks, 7(40), 1-19.


• Galai, D. and R. Masulis (1976). The Option Pricing Model and the Risk Factor of The Journal of Financial Economics, Vol. 3 No. (1-2). pp 53-81.


• Gang,K. Xiangrui ,C. Peng,Y. Alsaadi, F. E. Enrique, V.(2019). Machine learning methods for systemic risk analysis in financial sectorsmTechnological and Economic Development of Economy; Vilnius 25(5),  716-742.


• Giudici, P., & Parisi, L. (2017). Sovereign risk in the Euro area: a multivariate stochastic process approach. Quantitativ Finance, 17(12), 1995-2008.


• Haldane, A. G., & May, R. M. (2011). Systemic Risk in Banking Ecosystems, Nature.


• Huang,J.Z, Shi, Z. Zhou, H.(2019). Specication Analysis of Structural Credit Risk Models, Review of Finance forthcoming , 1(61), pp1-61.


• John, W. Muteba, M. Hammoudeh, Sh. Gupta, R.(2017). Financial Tail Risks in Conventional and Islamic Stock Markets: A Comparative Analysis, Pacific-Basin Finance Journal,


• Jurczyk J, Eckrot A, Morgenstern I. (2016). Quantifying systemic risk by solutions of themean-variance risk PLoS One. 11(6):e0158444. https://doi.org/10.1371/journal.pone.0158444 PMID: 27351482.


• Knif, Johan, James W. Kolari and Seppo Pynnönen. (2009). Assets Pricing with Exchange and Inflation www.ssrn.com.


• Kochanek K, Tynan S.(2010). The environmental risk assessment for decision support systemfor waterman-agement in the vicinity of open cast mines (DSWMVOC). Ukio Technologinis Ir Ekonominis Vystymas3(16),.:31-414.


• Kociu L, Mano R, Hysi A. (2015). Financial risk assessment of albanian SMEs with the help of financial ratio (acase study-SME-s in Gjirokasra region). European Scientific Journal. 2015; 11:309–21.


• Koua, G., Peng, Lu, CH.(2014). MCDM approach to evaluating bank loan default models. Technological and Economic Development of Economy, 20:2, 292-311.


• Ladley, D. (2013). Contagion and risk-sharing on the inter-bank market. Journal of Economic Dynamics and Control, 37(7), 1384-1400.


• Laeven, L., Ratnovski, L., & Tong, H. (2016). Bank size, capital, and systemic risk: Some international evidence. Journal of Banking & Finance, 69, 25-34.


• Lee PT-W, Lin C-W, Shin S-H. A comparative study on financial positions of shipping companies in Tai-wan and Korea using entropy and grey relation analysis. Expert Systems with Applications. 2012; 39(5):5649–57.


• Maria Dinu,A.(2014). Risk in financial transactions and financial risk management, ocial and Behavioral Sciences 116 ,2458 – 2461.


• Miles, J. A. (1986). Growth Options and the Real Determinants of Systematic Risk, Journal of Business Finance and Accounting, 13 No. 1. pp. 95–105.


• Randall, M, Thompson, S. (2017). IFRS 9 Impairment: Significant Increase in Credit Risk: PwC in Depth. Available online: www.pwc.com (accessed on 5 April 2019..


• ShaverdiM, Ramezani I, Tahmasebi R, Rostamy AAA. (2016).Combining fuzzy AHP and fuzzy TOPSIS with financial ratios to design a novel performance evaluationmodel. International Journal of Fuzzy Systems. 18(2):248–62.


• Tian Z-P,Wang J-Q, Zhang H-Y. (2018).An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy and VIKOR methods. Applied Soft Computing.


• Wang J,Wang J-Q, Tian Z-P, Zhao D-Y. (2018).A multihesitant fuzzy linguisticmulticriteria decision-making approach for logistics outsourcing with incomplete weight information. International Transactions in Operational Research. 2018; 25:831–56.


• Wang M, Liu P. An extended VIKOR method for investment risk assessment of real estate based on theدuncertain linguistic variable Advances in Information Sciences and Service Sciences. 2011; 3(7):35–