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BIST 30 ENDEKSİNDE PORTFÖY SEÇİMİ İÇİN YENİ BİR KISMİ HEDEF PROGRAMLAMA YAKLAŞIMI

Year 2018, Volume: 7 Issue: 13, 119 - 134, 28.01.2018

Abstract




Finansal portföy
seçim problemi her zaman yatırımcılar ve finansal kurumlar için çözülmesi zor
ve önemli bir konudur. Portföy seçimi sorununun özü, belirli kriterler
çerçevesinde optimum portföy bileşimi elde etmektir. Kriterler ve kriterlere
ait önem dereceleri yatırımcıların bakış açısına göre değişebilmekteyken,
portföyün temel değerlendirme unsuru, getiri ve risk unsurlarından
oluşmaktadır. Modern portföy teorisine göre sırasıyla portföy ortalama ve
varyansı bu faktörleri karşılamaktadır. Markowitz, portföy seçiminde, hisse
senedi getiri serilerinin normal olarak dağıldığı ve karar vericilerin fayda
fonksiyonlarının karesel olduğu varsayımına dayanan bir ortalama varyans modeli
önermiştir. İlgili varsayımların geçerli olmadığı ve hisse senetlerinin
çarpıklık ve basıklık değerlerinin anlamlı olduğu pazarlarda yapılan
araştırmalar literatürde yaygın olarak görülmektedir.  Ortalama varyans modeline yüksek momentler ve
entropi fonksiyonlarının eklenmesi ile portföy seçim sürecine daha fazla
dağılım bilgisi ve çeşitlilik katılabilmektedir. BIST-30 Endeksi portföy seçim
probleminde, Polinomsal Hedef Programlama modeli ve önerilen Kısmi Hedef
Programlama yaklaşımı, ortalama varyans çarpıklık basıklık entropi fonksiyonlarını
barındıran portföy seçim sürecinde test edilmiştir. Önerilen modelin gerçek
performansı ölçülmüş ve etkin portföy oluşturma açısından iyi sonuçlar verdiği
gözlemlenmiştir.




References

  • Aouni, Belaı̈d, ve Ossama Kettani. (2001) "Goal programming model: A glorious history and a promising future." European Journal of Operational Research 133.2 :225-231.
  • Aracioglu, B., Demircan, F. ve Soyuer, H. (2011). Mean-Variance-Skewness-Kurtosis Approach to Portfolio Optimization: An Application in Istanbul Stock Exchange/Portföy Optimizasyonunda Ortalama-Varyans-Çarpiklik-BasiklikYaklasimi: IMKB Uygulamasi. Ege Akademik Bakis, 11, 9.
  • Arditti, F. D., ve Levy, H. (1975). Portfolio efficiency analysis in three moments: the multiperiod case. The Journal of Finance, 30(3), 797-809.
  • Bera, A. K., ve Park, S. Y. (2008). Optimal portfolio diversification using the maximum entropy principle. Econometric Reviews, 27(4-6), 484-512.
  • BIST web site. Available online:https://datastore.borsaistanbul.com/ (accessed on 17 March 2017)
  • Caporin, M., Jannin, G. M., Lisi, F., & Maillet, B. B. (2014). A survey on the four families of performance measures. Journal of Economic Surveys, 28(5), 917-942.
  • Carmichael, B., Koumou, G., ve Moran, K. (2015). Unifying Portfolio Diversification Measures Using Rao's Quadratic Entropy. CİRANO. Scientific Series. Montreal.
  • Charnes, A. ve Cooper W.W. (1961) Management models and industrial applications of linear programming, Wiley, New York.
  • Charnes, A. ve Cooper, W. W. (1977). Goal programming and multiple objective optimizations: Part 1. European Journal of Operational Research, 1(1), 39-54.
  • Chunhachinda, P., Dandapani, K., Hamid, S., ve Prakash, A. J. (1997). Portfolio selection and skewness: Evidence from international stock markets. Journal of Banking & Finance, 21(2), 143-167.
  • Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576-586.
  • DeMiguel, V., Garlappi, L., ve Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?. Review of Financial Studies, 22(5), 1915-1953.
  • Harvey, C. R., Liechty, J. C., Liechty, M. W., ve Müller, P. (2010). Portfolio selection with higher moments. Quantitative Finance, 10(5), 469-485.
  • Ijiri, Y. Management Goals and Accounting for Control, North Holland, Amsterdam, 1965.
  • Jääskeläinen, V. (1969). A goal programming model of aggregate production planning. The Swedish Journal of Economics, 14-29.
  • Jana, P., Roy, T. K., ve Mazumder, S. K. (2007). Multi-objective mean-variance-skewness model for portfolio optimization. Advanced Modeling and Optimization, 9(1), 181-193.
  • Jurczenko, E., Maillet, B. B., ve Merlin, P. (2005). Hedge funds portfolio selection with higher-order moments: a non-parametric mean-variance-skewness-kurtosis efficient frontier. Available at SSRN 676904.
  • Kemalbay, G., Özkut, C. M., ve Franko, C. (2011). Portfolio selection with higher moments: A polynomial goal programming approach to ISE-30 index. Ekonometri ve Istatistik Dergisi, (13), 41.
  • Konno, H., ve Suzuki, K. I. (1995). A mean-variance-skewness portfolio optimization model. Journal of the Operations Research Society of Japan, 38(2), 173-187.
  • Lai, K. K., Yu, L., ve Wang, S. (2006, June). Mean-variance-skewness-kurtosis-based portfolio optimization. In Computer and Computational Sciences, 2006. IMSCCS'06. First International Multi-Symposiums on (Vol. 2, pp. 292-297). IEEE.
  • Lai, T. Y. (1991). Portfolio selection with skewness: a multiple-objective approach. Review of Quantitative Finance and Accounting, 1(3), 293-305.
  • Leung, M. T., Daouk, H., ve Chen, A. S. (2001). Using investment portfolio return to combine forecasts: A multiobjective approach. European Journal of Operational Research, 134(1), 84-102.
  • Maringer, D., ve Parpas, P. (2009). Global optimization of higher order moments in portfolio selection. Journal of Global Optimization, 43(2-3), 219-230.
  • Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.
  • Markowitz, H. M. (1991). Foundations of portfolio theory. The journal of finance, 46(2), 469-477.
  • Mhiri, M., ve Prigent, J. L. (2010). International portfolio optimization with higher moments. International Journal of Economics and Finance, 2(5), 157.
  • Prakash, A. J., Chang, C. H., ve Pactwa, T. E. (2003). Selecting a portfolio with skewness: Recent evidence from US, European, and Latin American equity markets. Journal of Banking & Finance, 27(7), 1375-1390.
  • Romero, C. (1986). A survey of generalized goal programming (1970–1982). European Journal of Operational Research, 25(2), 183-191.
  • Samuelson, P. A. (1970). The fundamental approximation theorem of portfolio analysis in terms of means, variances and higher moments. The Review of Economic Studies, 37(4), 537-542.
  • Simkowitz, M. A., ve Beedles, W. L. (1978). Diversification in a three-moment world. Journal of Financial and Quantitative Analysis, 13(05), 927-941.
  • Singleton, J. C., ve Wingender, J. (1986). Skewness persistence in common stock returns. Journal of Financial and Quantitative Analysis, 21(03), 335-341.
  • Steinbach, M. C. (2001). Markowitz revisited: Mean-variance models in financial portfolio analysis. SIAM review, 43(1), 31-85.
  • Tamiz, M., Jones, D. ve Romero, C. (1998). Goal programming for decision making: An overview of the current state-of-the-art. European Journal of operational research, 111(3), 569-581.
  • Tayi, G. K., ve Leonard, P. A. (1988). Bank balance-sheet management: An alternative multi-objective model. Journal of the Operational Research Society, 39(4), 401-410.
  • Usta, I., ve Kantar, Y. M. (2011). Mean-variance-skewness-entropy measures: A multi-objective approach for portfolio selection. Entropy, 13(1), 117-133.
  • Yue, W., ve Wang, Y. (2017). A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios. Physica A: Statistical Mechanics and its Applications, 465, 124-140.
  • Zanakis, S. H. ve Gupta, S. K. (1985). A categorized bibliographic survey of goal programming. Omega, 13(3), 211-222.
  • Watanabe Y. (2006), “Is Sharpe Ratio Still Effective?”, Journal of Performance Measurement, vol. 11, n° 1, pp. 55-66.
Year 2018, Volume: 7 Issue: 13, 119 - 134, 28.01.2018

Abstract

References

  • Aouni, Belaı̈d, ve Ossama Kettani. (2001) "Goal programming model: A glorious history and a promising future." European Journal of Operational Research 133.2 :225-231.
  • Aracioglu, B., Demircan, F. ve Soyuer, H. (2011). Mean-Variance-Skewness-Kurtosis Approach to Portfolio Optimization: An Application in Istanbul Stock Exchange/Portföy Optimizasyonunda Ortalama-Varyans-Çarpiklik-BasiklikYaklasimi: IMKB Uygulamasi. Ege Akademik Bakis, 11, 9.
  • Arditti, F. D., ve Levy, H. (1975). Portfolio efficiency analysis in three moments: the multiperiod case. The Journal of Finance, 30(3), 797-809.
  • Bera, A. K., ve Park, S. Y. (2008). Optimal portfolio diversification using the maximum entropy principle. Econometric Reviews, 27(4-6), 484-512.
  • BIST web site. Available online:https://datastore.borsaistanbul.com/ (accessed on 17 March 2017)
  • Caporin, M., Jannin, G. M., Lisi, F., & Maillet, B. B. (2014). A survey on the four families of performance measures. Journal of Economic Surveys, 28(5), 917-942.
  • Carmichael, B., Koumou, G., ve Moran, K. (2015). Unifying Portfolio Diversification Measures Using Rao's Quadratic Entropy. CİRANO. Scientific Series. Montreal.
  • Charnes, A. ve Cooper W.W. (1961) Management models and industrial applications of linear programming, Wiley, New York.
  • Charnes, A. ve Cooper, W. W. (1977). Goal programming and multiple objective optimizations: Part 1. European Journal of Operational Research, 1(1), 39-54.
  • Chunhachinda, P., Dandapani, K., Hamid, S., ve Prakash, A. J. (1997). Portfolio selection and skewness: Evidence from international stock markets. Journal of Banking & Finance, 21(2), 143-167.
  • Contini, B. (1968). A stochastic approach to goal programming. Operations Research, 16(3), 576-586.
  • DeMiguel, V., Garlappi, L., ve Uppal, R. (2009). Optimal versus naive diversification: How inefficient is the 1/N portfolio strategy?. Review of Financial Studies, 22(5), 1915-1953.
  • Harvey, C. R., Liechty, J. C., Liechty, M. W., ve Müller, P. (2010). Portfolio selection with higher moments. Quantitative Finance, 10(5), 469-485.
  • Ijiri, Y. Management Goals and Accounting for Control, North Holland, Amsterdam, 1965.
  • Jääskeläinen, V. (1969). A goal programming model of aggregate production planning. The Swedish Journal of Economics, 14-29.
  • Jana, P., Roy, T. K., ve Mazumder, S. K. (2007). Multi-objective mean-variance-skewness model for portfolio optimization. Advanced Modeling and Optimization, 9(1), 181-193.
  • Jurczenko, E., Maillet, B. B., ve Merlin, P. (2005). Hedge funds portfolio selection with higher-order moments: a non-parametric mean-variance-skewness-kurtosis efficient frontier. Available at SSRN 676904.
  • Kemalbay, G., Özkut, C. M., ve Franko, C. (2011). Portfolio selection with higher moments: A polynomial goal programming approach to ISE-30 index. Ekonometri ve Istatistik Dergisi, (13), 41.
  • Konno, H., ve Suzuki, K. I. (1995). A mean-variance-skewness portfolio optimization model. Journal of the Operations Research Society of Japan, 38(2), 173-187.
  • Lai, K. K., Yu, L., ve Wang, S. (2006, June). Mean-variance-skewness-kurtosis-based portfolio optimization. In Computer and Computational Sciences, 2006. IMSCCS'06. First International Multi-Symposiums on (Vol. 2, pp. 292-297). IEEE.
  • Lai, T. Y. (1991). Portfolio selection with skewness: a multiple-objective approach. Review of Quantitative Finance and Accounting, 1(3), 293-305.
  • Leung, M. T., Daouk, H., ve Chen, A. S. (2001). Using investment portfolio return to combine forecasts: A multiobjective approach. European Journal of Operational Research, 134(1), 84-102.
  • Maringer, D., ve Parpas, P. (2009). Global optimization of higher order moments in portfolio selection. Journal of Global Optimization, 43(2-3), 219-230.
  • Markowitz, H. (1952). Portfolio selection. The journal of finance, 7(1), 77-91.
  • Markowitz, H. M. (1991). Foundations of portfolio theory. The journal of finance, 46(2), 469-477.
  • Mhiri, M., ve Prigent, J. L. (2010). International portfolio optimization with higher moments. International Journal of Economics and Finance, 2(5), 157.
  • Prakash, A. J., Chang, C. H., ve Pactwa, T. E. (2003). Selecting a portfolio with skewness: Recent evidence from US, European, and Latin American equity markets. Journal of Banking & Finance, 27(7), 1375-1390.
  • Romero, C. (1986). A survey of generalized goal programming (1970–1982). European Journal of Operational Research, 25(2), 183-191.
  • Samuelson, P. A. (1970). The fundamental approximation theorem of portfolio analysis in terms of means, variances and higher moments. The Review of Economic Studies, 37(4), 537-542.
  • Simkowitz, M. A., ve Beedles, W. L. (1978). Diversification in a three-moment world. Journal of Financial and Quantitative Analysis, 13(05), 927-941.
  • Singleton, J. C., ve Wingender, J. (1986). Skewness persistence in common stock returns. Journal of Financial and Quantitative Analysis, 21(03), 335-341.
  • Steinbach, M. C. (2001). Markowitz revisited: Mean-variance models in financial portfolio analysis. SIAM review, 43(1), 31-85.
  • Tamiz, M., Jones, D. ve Romero, C. (1998). Goal programming for decision making: An overview of the current state-of-the-art. European Journal of operational research, 111(3), 569-581.
  • Tayi, G. K., ve Leonard, P. A. (1988). Bank balance-sheet management: An alternative multi-objective model. Journal of the Operational Research Society, 39(4), 401-410.
  • Usta, I., ve Kantar, Y. M. (2011). Mean-variance-skewness-entropy measures: A multi-objective approach for portfolio selection. Entropy, 13(1), 117-133.
  • Yue, W., ve Wang, Y. (2017). A new fuzzy multi-objective higher order moment portfolio selection model for diversified portfolios. Physica A: Statistical Mechanics and its Applications, 465, 124-140.
  • Zanakis, S. H. ve Gupta, S. K. (1985). A categorized bibliographic survey of goal programming. Omega, 13(3), 211-222.
  • Watanabe Y. (2006), “Is Sharpe Ratio Still Effective?”, Journal of Performance Measurement, vol. 11, n° 1, pp. 55-66.
There are 38 citations in total.

Details

Journal Section AÇIK ERİŞİM POLİTİKASI
Authors

Mehmet Aksaraylı

Osman Pala This is me

Publication Date January 28, 2018
Acceptance Date December 21, 2017
Published in Issue Year 2018 Volume: 7 Issue: 13

Cite

APA Aksaraylı, M., & Pala, O. (2018). BIST 30 ENDEKSİNDE PORTFÖY SEÇİMİ İÇİN YENİ BİR KISMİ HEDEF PROGRAMLAMA YAKLAŞIMI. Balkan Sosyal Bilimler Dergisi, 7(13), 119-134.