Portfolio selection problem: a comparison of fuzzy goal programming and linear physical programming
DOI:
https://doi.org/10.11121/ijocta.01.2016.00284Keywords:
Portfolio selection problem, linear physical programming fuzzy goal programmingAbstract
Investors have limited budget and they try to maximize their return with minimum risk. Therefore this study aims to deal with the portfolio selection problem. In the study two criteria are considered which are expected return, and risk. In this respect, linear physical programming (LPP) technique is applied on Bist 100 stocks to be able to find out the optimum portfolio. The analysis covers the period April 2009- March 2015. This period is divided into two; April 2009-March 2014 and April 2014 – March 2015. April 2009-March 2014 period is used as data to find an optimal solution. April 2014-March 2015 period is used to test the real performance of portfolios. The performance of the obtained portfolio is compared with that obtained from fuzzy goal programming (FGP). Then the performances of both method, LPP and FGP are compared with BIST 100 in terms of their Sharpe Indexes. The findings reveal that LPP for portfolio selection problem is a good alternative to FGP.Downloads
References
Ertuna, I.O., Yatırım ve Portföy Analizi (Bilgisayar Uygulama Örnekleriyle), Boğaziçi Universitesi, (1991).
Korkmaz, T, Ceylan, A., Sermaye Piyasası ve Menkul Değer Analizi, Ekin Yayınevi, Bursa (2007).
İskenderoğlu, Ö., Karadeniz, E., Optimum Portföyün Seçimi: İMKB 30 Üzerinde Bir Uygulama, CÜ İktisadi ve İdari Bilimler Dergisi Cilt 12, Sayı:2 (2011).
Parra, M. A., Terol, A. B., Uria, M.V. R., A Fuzzy Goal Programming Approach to Portfolio Selection, European Journal of Operational Research,133, 287-297 (2001). Crossref
Alinezhad, A., Zohrehbandian, M., Kian, M., Ekhtiari, M., Esfandiari, N., Extension of Portfolio Selection Problem with Fuzzy Goal Programming: A Fuzzy Allocated Portfolio Apprach, Journal Of Optimization in Industrial Engineering, 9, 69-76 (2011).
Sharpe, W. F., "A Simplified Model For Portfolio Analysis", Management Science, Vol 9; 277-293 (1963). Crossref
Sharpe,, W. F., Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, Journal of Finance, 19, 425–442 (1964). Crossref
Lintler, J., "The Valuation of Risky Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets", Review of Economics and Statistics, No: 47, s. 13–37 (1965).
Ross, C., The Arbirtage Theory of Capital Asset Pricing, Journal of Economic Theory, 3, 341-360, (1976). Crossref
Huberman G, Arbitrage Pricing Theory A simple Approach, Journal of Economic Theory, 28, 183-191 (1982). Crossref
Sharpe,, W. F., Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk, Journal of Finance, , 19, 425–442 (1964). Crossref
Lintler, J., "The Valuation of Risky Assets and the Selection of Risky Investments in Stock Portfolios and Capital Budgets", Review of Economics and Statistics, , No: 47, s. 13–37 (1965).
Ross, C., The Arbirtage Theory of Capital Asset Pricing, Journal of Economic Theory, 3, 341-360, (1976). Crossref
Huberman G, Arbitrage Pricing Theory A simple Approach, Journal of Economic Theory, 28, 183-191 (1982). Crossref
Sharpe, W. F., A Liner Programming Algorithm For a Mutual Fund Portfolio Selection, Management Science, 13, 499-510 (1967). Crossref
Sharpe, W. F., A Linear Programming Approximation For The General Portfolio Analysis Problem, Journal of Financial an Quantitative Analysis: 1263-1275 (1971). Crossref
Stone, B., K., A Linear Programming Formulation of The General Portfolio Selection Problem, Journal of Financial an Quantative Analysis, 8: 621-636 (1973). Crossref
Tanaka, H., Hayashi, I., Watada,J. Possibilistic linear regression analysis for fuzzy data, European Journal of Operation Research, 40: 389-396 (1989). Crossref
Konno H. ve Yamazaki H., Mean-Absolute Deviation Portfolio Optimization Model and Its Applications to Tokyo Stock Market, Management Science, 37 (5), ss. 519 531 (1991).
Young MR, A minimax portfolio selection rule with linear programming solution. Management Science, 44: 673-683 (1998). Crossref
Cai XQ, Teo KL, Yang XQ, Zhou XY, Portfolio optimization under a minimax rule. Management Sci. 46: 957-972 (2000). Crossref
Cai X, Teo KL, Yang XQ and Zhou XY, Minimax portfolio optimizaiton: empirical numerical study. J. Opl Res Soc 55: 65-72 (2004). Crossref
Parra, M. A., Terol, A. B., Uria, M.V. R., A Fuzzy Goal Programming Approach to Portfolio Selection, European Journal of Operational Research,133, 287-297 (2001). Crossref
Alinezhad, A., Zohrehbandian, M., Kian, M., Ekhtiari, M., Esfandiari, N., Extension of Portfolio Selection Problem with Fuzzy Goal Programming: A Fuzzy Allocated Portfolio Apprach, Journal Of Optimization in Industrial Engineering, 9, 69-76 (2011).
Mirakhorli, A., Farahani M. H. , Ramtirr F., New Approach in Supplier Selection Using Linear Physical Programming, IEEE, 47-51 (2009).
Lai X, Xie M, Tan KC, Optimizing product design using quantitative quality function deployment: a case study. Qual Reliability Eng Int 23:45–572 (2007). Crossref
Kongar, E.; Gupta, S.M., Disassembly-to-order system using Linear Physical Programming, Electronics and the Environment, 2002 IEEE International Symposium on , vol., no., pp.312,317 (2002).
Messac, A., Gupta, S. M., and Akbulut, B., Linear Physical Programming: Effective Optimization for Complex Linear Systems, Transactions on Operational Research, Vol. 8, NO. 2, 39-59 (1996).
Zimmerman, H.J., Fuzzy programming and linear programming with several objective functions, Fuzzy Sets and Systems, Vol 1, No.1, 45-55 (1978). Crossref
Mirakhorli, A., Farahani M. H. , Ramtirr F., New Approach in Supplier Selection Using Linear Physical Programming, IEEE, 47-51 (2009).
Sharpe, W. F. A Simplified Model For Portfolio Analysis, Management Science, Vol 9; 277-293, (1963). Crossref
Downloads
Published
How to Cite
Issue
Section
License
Articles published in IJOCTA are made freely available online immediately upon publication, without subscription barriers to access. All articles published in this journal are licensed under the Creative Commons Attribution 4.0 International License (click here to read the full-text legal code). This broad license was developed to facilitate open access to, and free use of, original works of all types. Applying this standard license to your work will ensure your right to make your work freely and openly available.
Under the Creative Commons Attribution 4.0 International License, authors retain ownership of the copyright for their article, but authors allow anyone to download, reuse, reprint, modify, distribute, and/or copy articles in IJOCTA, so long as the original authors and source are credited.
The readers are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material
- for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
under the following terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
This work is licensed under a Creative Commons Attribution 4.0 International License.
