Formation of securities portfolio under conditions of uncertainty
DOI:
https://doi.org/10.15587/1729-4061.2017.92283Keywords:
securities portfolio, fuzzy cost of assets, minimax model, probabilistic fractional-linear criterionAbstract
We examined a problem on the formation of securities portfolio. A criterion for portfolio effectiveness is determined – a probability that the total portfolio profitability exceeds a threshold. In connection with real shortage of the volume of initial data, we substantiated the rejection of hypothesis about the normality of their distribution law and the problem is solved under assumption about the worst distribution density of these data. In this case, it is accepted that mathematical expectation and the dispersion of values for the cost of assets are the fuzzy numbers. The form of membership function of the fuzzy parameters in the problem is selected. We constructed an analytical expression to describe the criterion in the terms of fuzzy mathematics.
In this case, a problem on the maximization of fractional-quadratic functional with linear constraints is obtained. We devised a method for solving the obtained fuzzy problem of mathematical programming, which reduces this problem to the conventional problem of nonlinear programming. In order to solve this problem, it is proposed to employ the optimization method of zero order. It is demonstrated that the portfolio risk depends quadratically on the mathematical expectation of its profitability. Recommendations are given regarding the choice of numerical value for the mathematical expectation of portfolio profitability depending on the acceptable portfolio risk.
References
- Gurin, L. S., Dymarskij, Ja. S., Merkulov, A. D. (1968). Zadachi i metody optimal'nogo raspredelenija resursov. Moscow: Sov. radio, 368.
- Larichev, O. I. (2002). Teorija i metody prinjatija reshenij. Moscow: Logos, 392.
- Bazaraa, M. S., Shetty, C. M. (1979). Nonhnear Programming: Theory and Algorithms. New York: Wiley, 560.
- Raskin, L. G. (1976). Analiz slozhnyh sistem i jelementy teorii upravlenija. Moscow: Sov. radio, 344.
- Karmanov, V. G. (1980). Matematicheskoe programmirovanie. Moscow: Gl. red. fiz.-mat. lit., 256.
- Himmelblau, D. (1972). Applied Nonlinear Programming. New York: McGraw-Hill, 416.
- Zangwill, W. I. (1969). Nonlinear Programmmg: A Unified Approach. PrenticeHall, Englewood Chffs, N. J., 356.
- Judin, D. B. (1974). Matematicheskie metody upravlenija v uslovijah nepolnoj informacii. Zadachi i metody stohasticheskogo programmirovanija. Moscow: Sov. radio, 392.
- Demuckij, V. P., Pignastyj, O. M. (2003). Teorija predprijatija. Ustojchivost' funkcionirovanija massovogo proizvodstva i prodvizhenija produkcii na rynok. Kharkiv: KhNU im. V. N. Karazina, 272.
- Demuckij, V. P., Pignastaja, V. S., Pignastyj, O. M. (2005). Stohasticheskoe opisanie jekonomiko-proizvodstvennyh sistem s massovym vypuskom produkcii. Doklady Nac. Akad. Nauk Ukrainy, 7, 66–71.
- Pignastyj, O. M. (2007). Stohasticheskaja teorija proizvodstvennyh sistem. Kharkiv: KhNU im. V. N. Karazina, 387.
- Raskin, L. G., Kirichenko, I. O., Seraja, O. V. (2013). Prikladnoe kontinual'noe linejnoe programmirovanie. Kharkiv, 293.
- Seraya, O. V., Demin, D. A. (2012). Linear Regression Analysis of a Small Sample of Fuzzy Input Data. Journal of Automation and Information Sciences, 44 (7), 34–48. doi: 10.1615/jautomatinfscien.v44.i7.40
- Raskin, L. G., Seraja, O. V. (2003). Formirovanie skaljarnogo kriterija predpochtenija po rezul'tatam poparnyh sravnenij ob’ektov. Vestnik NTU «KhPI», 63–68.
- Connor, G., Goldberg, L. R., Korajczyk, R. A. (2010). Portfolio Risk Analysis. Princeton University Press, 354. doi: 10.1515/9781400835294
- Otani, Y., Imai, J. (2013). Pricing Portfolio Credit Derivatives with Stochastic Recovery and Systematic Factor. IAENG International Journal of Applied Mathematics, 43 (4), 176–184.
- Read, C. (2012). The Portfolio Theorists: von Neumann, Savage, Arrow and Markowitz. Springer, 240. doi: 10.1057/9780230362307
- Bellman, R. E., Zadeh, L. A. (1970). Decision-Making in a Fuzzy Environment. Management Science, 17 (4), B–141–B–164. doi: 10.1287/mnsc.17.4.b141
- Negojce, K. (1981). Primenenie teorii sistem k problemam upravlenija. Moscow: MIR, 219.
- Orlovskij, S. A. (1981). Problemy prinjatija reshenij pri nechetkoj informacii. Moscow: Nauka, 264.
- Zajchenko, Ju. P. (1991). Issledovanie operacij. Nechetkaja optimizacija. Kyiv: Vishha shkola, 191.
- Raskin, L. G., Seraja, O. V. (2008). Nechetkaja matematika. Kharkiv: Parus, 352.
- Raskin, L., Sira, O. (2016). Method of solving fuzzy problems of mathematical programming. Eastern-European Journal of Enterprise Technologies, 5 (4 (83)), 23–28. doi: 10.15587/1729-4061.2016.81292
- Lukashin, Ju. P. (2003). Adaptivnye metody kratkosrochnogo prognozirovanija vremennyh rjadov. Moscow: Finansy i statistika, 416.
- Kostenko, Ju. T., Raskin, L. G. (1996). Prognozirovanie tehnicheskogo sostojanija sistem upravlenija. Kharkiv: Osnova, 303.
- Hank, D. Je. (2003). Biznes – prognozirovanie. Moscow: «Vil'jams», 656.
- Pawlak, Z. (1982). Rough sets. International Journal of Computer & Information Sciences, 11 (5), 341–356. doi: 10.1007/bf01001956
- Raskin, L., Sira, O. (2016). Fuzzy models of rough mathematics. Eastern-European Journal of Enterprise Technologies, 6 (4 (84)), 53–60. doi: 10.15587/1729-4061.2016.86739
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2017 Oksana Sira, Tetiana Katkova
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.