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PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI

Year 2025, Issue: 67, 101 - 113

Diler Türkoğlu

Abstract

Portföy optimizasyonu, yatırımcıların risk ve getiri hedeflerine ulaşabilmesi için önemli bir konu olup, bu alanda çeşitli stratejiler geliştirilmiştir. Bu çalışma, 21.11.2022 ile 16.07.2024 tarihleri arasında Borsa İstanbul Sürdürülebilirlik 25 Endeksi'nde işlem gören firmaların hisse senetleri üzerinden Parçacık Sürü Optimizasyonu algoritması kullanarak hem yatırımcı senaryoları geliştirmeyi hem de portföy optimizasyonu yapmayı amaçlamaktadır. Düşük, orta ve yüksek risk seviyelerine göre oluşturulan senaryolar, yatırımcıların farklı risk toleranslarına uygun stratejiler geliştirmelerine olanak tanımaktadır. Optimal portföy önerisi, belirli hisselerin yüksek ağırlıklarıyla dengeli bir yapı sunmakta ve yatırımcılara makul bir risk-getiri dengesi sağlamaktadır. Çalışma, yatırımcıların bilinçli kararlar almasına yardımcı olmayı, sürdürülebilir yatırımların önemini vurgulamayı ve piyasa koşullarına uygun stratejiler geliştirmeyi hedeflemektedir. Sonuç olarak, bu araştırmanın hem portföy optimizasyonu süreçlerinin iyileştirilmesi hem de yatırımcı senaryolarının oluşturulması açısından önem arz ettiği düşünülmektedir.

Keywords

Portföy optimizasyonu Parçacık sürü optimizasyonu Yatırımcı senaryosu

References

  • Alagöz, A., & Kutlu, M. (2012). Parçacik sürü optimizasyonu yaklaşimi ile emtia piyasasinda portföy optimizasyonu. Sosyal Ekonomik Araştırmalar Dergisi, 12(23), 35-50.
  • Bağcı, B., & Konak, F. (2016). Linear programming on portfolio optimization: Empirical evidence from bist mining industry index. Global Journal of Management and Business Research , 16(2), 31-36.
  • Ballestero, E. (2005). Mean‐semivariance efficient frontier: a downside risk model for portfolio selection. Applied Mathematical Finance, 12(1), 1-15. doi:10.1080/1350486042000254015
  • Bansal, J. C., Singh, P. K., Saraswat, M., Verma, A., Jadon, S. S., & Abraham, A. (2011, October). Inertia weight strategies in particle swarm optimization. In 2011 Third world congress on nature and biologically inspired computing (pp. 633-640). IEEE.
  • Borsa İstanbul. (2024). 2024 tarihinde Sürdürülebilirlik Endeksleri: https://borsaistanbul.com/tr/sayfa/165/bist-surdurulebilirlik-endeksleri adresinden alındı
  • Bülbül, M. (2023). Kuru fasulye tohumlarının çok sınıflı sınıflandırılması için hibrit bir yaklaşım. Journal of the Institute of Science and Technology, 13(1), 33-43. doi:10.21597/jist.1185949
  • Bülbül, M. A., Öztürk, C., & Işık, M. F. (2022). ptimization of climatic conditions affecting determination of the amount of water needed by plants in relation to their life cycle with particle swarm optimization, and determining the optimum irrigation schedule. The Computer Journal, 65(10), 2654-2663. doi:https://doi.org/10.1093/comjnl/bxab097
  • Carbon Gate. (2024, Haziran 11). 2024 tarihinde TSRS: TÜRKİYE SÜRDÜRÜLEBİLİRLİK RAPORLAMA STANDARDI: https://www.carbongate.io/blog/tsrs-turkiye-surdurulebilirlik-raporlama-standardi adresinden alındı
  • Chai, F. (2024). A Prediction Model for Credit Risk Measurement of Small and Micro Enterprises Based On Particle Swarm Optimization random forest algorithm. International Journal of Computer Science and Information Technology, 3(1), 227-234. doi: https://doi.org/10.62051/ijcsit.v3n1.29
  • Cura, T. (2009). Particle swarm optimization approach to portfolio optimization. Nonlinear analysis: Real world applications, 10(4), 2396-2406.
  • de Gusmão, R. P., & de Carvalho, F. D. A. (2019). Clustering of multi-view relational data based on particle swarm optimization. Expert Systems with Applications, 123, 34-53.
  • Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2009). Modern portfolio theory and investment analysis. John Wiley & Sons.
  • Erwin, K., & Engelbrecht, A. (2023). Meta-heuristics for portfolio optimization. Soft Computing, 27(24), 19045-19073. doi:10.1007/s00500-023-08177-x
  • Geambaşu, C., Şova, R., & Jianu, I. (2013). Rıik Measurement in Post-Modern Portfolio Theory: Differences From Modern Portfolio Theory. Economic Computation & Economic Cybernetics Studies & Research, 4(1).
  • Ishwa, A., Akshita, S., Dhruv, D., & Sharma , A. (2023). A Genetic Algorithm Approach for Portfolio Optimization. International Conference on Data Science and Applications (s. 113-124). Singapore: Springer Nature.
  • Jaaman, S., Lam, H., & Isa, Z. (2011). Different downside risk approaches in portfolio optimisation. Journal of Quality Measurement and Analysis JQMA, 7(1), 77-84.
  • Jarrah, M., & Ahmed , A.-K. (2024). A. Predicting Stock Market Index Using Bacterial Swarm Optimization for Enhanced Market Insights. Nature Inspired Optimization Theories (NIOT), 01(01), 44-53.
  • Jun, C. S., & Johar, F. Portfolio Optimization of Exchange-Traded Funds Listed on the New York Stock Exchange Using Particle Swarm Optimization.
  • Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
  • Kocadağlı, O., & Cinemre, N. (2010). Portföy optimizasyonunda SVFM ile bulanık doğrusal olmayan model yaklaşımı. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 39(2), 359-369.
  • Konak, F., & Bağcı, B. (2016). Fuzzy linear programming on portfolio optimization: empirical evidence from FTSE 100 Index. Global Journal of Management and Business Research: C Finance, 16(2), 65-69.
  • Liu, C., Shi, Y., & Xie, W. (2024). Applying Particle Swarm Optimization Algorithm to Solve Securities Portfolio Based on Utility Maximization. In 4th International Conference on New Computational Social Science (ICNCSS 2024) (s. 286-293). Atlantis Press.
  • Lv, M., Wang, J., Wang, S., Gao, J., & Guo, H. (2024). Developing a hybrid system for stock selection and portfolio optimization with many-objective optimization based on deep learning and improved NSGA-III. Information Sciences, 670(120549). doi:https://doi.org/10.1016/j.ins.2024.120549
  • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
  • Markowitz H. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons.
  • Meher, P., & Mishra, R. (2024). Risk-Adjusted Portfolio Optimization: Monte Carlo Simulation and Rebalancing. Australasian Accounting, Business and Finance Journal , 18(3), 85-101. doi:http://dx.doi.org/10.14453/aabfj.v18i3.06
  • Morteza, H., Jameii, S. M., & Sohrabi, M. K. (2023). An improved learning automata based multi-objective whale optimization approach for multi-objective portfolio optimization in financial markets. Expert Systems with Applications, 224, 119970
  • Özsoy, V. S. (2021). The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model. Engineering Computations, 38(4), 1933-1954.
  • Pekkaya, M. (2013). ARFIMA ve FIGARCH yöntemlerinin Markowitz ortalama varyans portföy optimizasyonunda kullanılması: İMKB-30 endeks hisseleri üzerine bir uygulama. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 42(1), 93-112.
  • R., S., & Mohideen, A. (2017). Modern Portfolio Theory: A Review of the Work Done on Performance Measures and their Role in Portfolio Construction. International Journal of Scientific Research in Science and Technology, 3(3), 525-532.
  • Reeves, W. T. (1983). Particle systems—a technique for modeling a class of fuzzy objects. ACM Transactions On Graphics (TOG), 2(2), 91-108.
  • Sayılgan, G. (2019). Soru ve Yanıtlarıyla İşletme Finansmanı (8. b.). Ankara: Siyasal Kitabevi. Sortino, F. A., & Price, L. N. (1994). Performance measurement in a downside risk framework. the Journal of Investing, 3(3), 59-64.
  • Sun, J., Fang, W., Wu, X., Lai, C.-H., & Xu, W. (2011). Solving the multi-stage portfolio optimization problem with a novel particle swarm optimization. Expert Systems with Applications, 38(6), 6727-6735. doi:https://doi.org/10.1016/j.eswa.2010.11.061
  • Syaripuddin, S., Amijaya, F., Wasono, W., Tulzahrah, S., & Suciati, R. (2024). Applıcatıon of quadratıc programmıng on portfolıo optımızatıon usıng wolfe’s method and partıcle swarm optımızatıon algorıthm. Jurnal Ilmu Matematika dan Terapan, 18(2), 1067-1080. doi:10.30598/barekengvol18iss2pp1067-1080
  • Zhu, H., Wang, Y., Wang, K., & Chen, Y. (2011). Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. Expert Systems with Applications, 38(8), 10161-10169. doi:https://doi.org/10.1016/j.eswa.2011.02.075

PORTFOLIO OPTIMIZATION AND INVESTOR SCENARIOS: APPLICATION OF PARTICLE SWARM OPTIMIZATION TO THE BIST SUSTAINABILITY 25 INDEX

Year 2025, Issue: 67, 101 - 113

Diler Türkoğlu

Abstract

Portfolio optimization is a key topic for investors to achieve their risk and return objectives, and various strategies have been developed in this field. The present research employs the Particle Swarm Optimization algorithm on the shares of firms listed in the Borsa Istanbul Sustainability 25 Index between November 21, 2022, and July 16, 2024, to develop investor scenarios and optimize portfolios. Investment plans appropriate for varying risk tolerances can be created by investors employing scenarios categorized as low, medium, or high risk. The optimal portfolio recommendation offers investors a balanced structure with high weights on certain stocks and a fair risk-return balance. The objectives of this research are to support investors in making well-informed decisions, highlight the significance of sustainable investments, and provide market-appropriate solutions. This makes the research significant for both developing investor scenarios and enhancing portfolio optimization procedures.

Keywords

Portfolio optimisation Particle swarm optimisation Investor scenario

References

  • Alagöz, A., & Kutlu, M. (2012). Parçacik sürü optimizasyonu yaklaşimi ile emtia piyasasinda portföy optimizasyonu. Sosyal Ekonomik Araştırmalar Dergisi, 12(23), 35-50.
  • Bağcı, B., & Konak, F. (2016). Linear programming on portfolio optimization: Empirical evidence from bist mining industry index. Global Journal of Management and Business Research , 16(2), 31-36.
  • Ballestero, E. (2005). Mean‐semivariance efficient frontier: a downside risk model for portfolio selection. Applied Mathematical Finance, 12(1), 1-15. doi:10.1080/1350486042000254015
  • Bansal, J. C., Singh, P. K., Saraswat, M., Verma, A., Jadon, S. S., & Abraham, A. (2011, October). Inertia weight strategies in particle swarm optimization. In 2011 Third world congress on nature and biologically inspired computing (pp. 633-640). IEEE.
  • Borsa İstanbul. (2024). 2024 tarihinde Sürdürülebilirlik Endeksleri: https://borsaistanbul.com/tr/sayfa/165/bist-surdurulebilirlik-endeksleri adresinden alındı
  • Bülbül, M. (2023). Kuru fasulye tohumlarının çok sınıflı sınıflandırılması için hibrit bir yaklaşım. Journal of the Institute of Science and Technology, 13(1), 33-43. doi:10.21597/jist.1185949
  • Bülbül, M. A., Öztürk, C., & Işık, M. F. (2022). ptimization of climatic conditions affecting determination of the amount of water needed by plants in relation to their life cycle with particle swarm optimization, and determining the optimum irrigation schedule. The Computer Journal, 65(10), 2654-2663. doi:https://doi.org/10.1093/comjnl/bxab097
  • Carbon Gate. (2024, Haziran 11). 2024 tarihinde TSRS: TÜRKİYE SÜRDÜRÜLEBİLİRLİK RAPORLAMA STANDARDI: https://www.carbongate.io/blog/tsrs-turkiye-surdurulebilirlik-raporlama-standardi adresinden alındı
  • Chai, F. (2024). A Prediction Model for Credit Risk Measurement of Small and Micro Enterprises Based On Particle Swarm Optimization random forest algorithm. International Journal of Computer Science and Information Technology, 3(1), 227-234. doi: https://doi.org/10.62051/ijcsit.v3n1.29
  • Cura, T. (2009). Particle swarm optimization approach to portfolio optimization. Nonlinear analysis: Real world applications, 10(4), 2396-2406.
  • de Gusmão, R. P., & de Carvalho, F. D. A. (2019). Clustering of multi-view relational data based on particle swarm optimization. Expert Systems with Applications, 123, 34-53.
  • Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2009). Modern portfolio theory and investment analysis. John Wiley & Sons.
  • Erwin, K., & Engelbrecht, A. (2023). Meta-heuristics for portfolio optimization. Soft Computing, 27(24), 19045-19073. doi:10.1007/s00500-023-08177-x
  • Geambaşu, C., Şova, R., & Jianu, I. (2013). Rıik Measurement in Post-Modern Portfolio Theory: Differences From Modern Portfolio Theory. Economic Computation & Economic Cybernetics Studies & Research, 4(1).
  • Ishwa, A., Akshita, S., Dhruv, D., & Sharma , A. (2023). A Genetic Algorithm Approach for Portfolio Optimization. International Conference on Data Science and Applications (s. 113-124). Singapore: Springer Nature.
  • Jaaman, S., Lam, H., & Isa, Z. (2011). Different downside risk approaches in portfolio optimisation. Journal of Quality Measurement and Analysis JQMA, 7(1), 77-84.
  • Jarrah, M., & Ahmed , A.-K. (2024). A. Predicting Stock Market Index Using Bacterial Swarm Optimization for Enhanced Market Insights. Nature Inspired Optimization Theories (NIOT), 01(01), 44-53.
  • Jun, C. S., & Johar, F. Portfolio Optimization of Exchange-Traded Funds Listed on the New York Stock Exchange Using Particle Swarm Optimization.
  • Kennedy, J., & Eberhart, R. (1995, November). Particle swarm optimization. In Proceedings of ICNN'95-international conference on neural networks (Vol. 4, pp. 1942-1948). IEEE.
  • Kocadağlı, O., & Cinemre, N. (2010). Portföy optimizasyonunda SVFM ile bulanık doğrusal olmayan model yaklaşımı. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 39(2), 359-369.
  • Konak, F., & Bağcı, B. (2016). Fuzzy linear programming on portfolio optimization: empirical evidence from FTSE 100 Index. Global Journal of Management and Business Research: C Finance, 16(2), 65-69.
  • Liu, C., Shi, Y., & Xie, W. (2024). Applying Particle Swarm Optimization Algorithm to Solve Securities Portfolio Based on Utility Maximization. In 4th International Conference on New Computational Social Science (ICNCSS 2024) (s. 286-293). Atlantis Press.
  • Lv, M., Wang, J., Wang, S., Gao, J., & Guo, H. (2024). Developing a hybrid system for stock selection and portfolio optimization with many-objective optimization based on deep learning and improved NSGA-III. Information Sciences, 670(120549). doi:https://doi.org/10.1016/j.ins.2024.120549
  • Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
  • Markowitz H. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons.
  • Meher, P., & Mishra, R. (2024). Risk-Adjusted Portfolio Optimization: Monte Carlo Simulation and Rebalancing. Australasian Accounting, Business and Finance Journal , 18(3), 85-101. doi:http://dx.doi.org/10.14453/aabfj.v18i3.06
  • Morteza, H., Jameii, S. M., & Sohrabi, M. K. (2023). An improved learning automata based multi-objective whale optimization approach for multi-objective portfolio optimization in financial markets. Expert Systems with Applications, 224, 119970
  • Özsoy, V. S. (2021). The determination of the most suitable inertia weight strategy for particle swarm optimization via the minimax mixed-integer linear programming model. Engineering Computations, 38(4), 1933-1954.
  • Pekkaya, M. (2013). ARFIMA ve FIGARCH yöntemlerinin Markowitz ortalama varyans portföy optimizasyonunda kullanılması: İMKB-30 endeks hisseleri üzerine bir uygulama. İstanbul Üniversitesi İşletme Fakültesi Dergisi, 42(1), 93-112.
  • R., S., & Mohideen, A. (2017). Modern Portfolio Theory: A Review of the Work Done on Performance Measures and their Role in Portfolio Construction. International Journal of Scientific Research in Science and Technology, 3(3), 525-532.
  • Reeves, W. T. (1983). Particle systems—a technique for modeling a class of fuzzy objects. ACM Transactions On Graphics (TOG), 2(2), 91-108.
  • Sayılgan, G. (2019). Soru ve Yanıtlarıyla İşletme Finansmanı (8. b.). Ankara: Siyasal Kitabevi. Sortino, F. A., & Price, L. N. (1994). Performance measurement in a downside risk framework. the Journal of Investing, 3(3), 59-64.
  • Sun, J., Fang, W., Wu, X., Lai, C.-H., & Xu, W. (2011). Solving the multi-stage portfolio optimization problem with a novel particle swarm optimization. Expert Systems with Applications, 38(6), 6727-6735. doi:https://doi.org/10.1016/j.eswa.2010.11.061
  • Syaripuddin, S., Amijaya, F., Wasono, W., Tulzahrah, S., & Suciati, R. (2024). Applıcatıon of quadratıc programmıng on portfolıo optımızatıon usıng wolfe’s method and partıcle swarm optımızatıon algorıthm. Jurnal Ilmu Matematika dan Terapan, 18(2), 1067-1080. doi:10.30598/barekengvol18iss2pp1067-1080
  • Zhu, H., Wang, Y., Wang, K., & Chen, Y. (2011). Particle Swarm Optimization (PSO) for the constrained portfolio optimization problem. Expert Systems with Applications, 38(8), 10161-10169. doi:https://doi.org/10.1016/j.eswa.2011.02.075
There are 35 citations in total.

Details

Primary Language Turkish
Subjects Finance, Investment and Portfolio Management
Journal Section Research Article
Authors

Diler Türkoğlu 0000-0001-5247-1590

Early Pub Date March 4, 2025
Publication Date
Submission Date October 1, 2024
Acceptance Date January 20, 2025
Published in Issue Year 2025 Issue: 67

Cite

APA Türkoğlu, D. (2025). PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(67), 101-113. https://doi.org/10.30794/pausbed.1559258
AMA Türkoğlu D. PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI. PAUSBED. March 2025;(67):101-113. doi:10.30794/pausbed.1559258
Chicago Türkoğlu, Diler. “PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, no. 67 (March 2025): 101-13. https://doi.org/10.30794/pausbed.1559258.
EndNote Türkoğlu D (March 1, 2025) PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 67 101–113.
IEEE D. Türkoğlu, “PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI”, PAUSBED, no. 67, pp. 101–113, March 2025, doi: 10.30794/pausbed.1559258.
ISNAD Türkoğlu, Diler. “PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 67 (March 2025), 101-113. https://doi.org/10.30794/pausbed.1559258.
JAMA Türkoğlu D. PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI. PAUSBED. 2025;:101–113.
MLA Türkoğlu, Diler. “PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, no. 67, 2025, pp. 101-13, doi:10.30794/pausbed.1559258.
Vancouver Türkoğlu D. PORTFÖY OPTİMİZASYONU VE YATIRIMCI SENARYOLARI: PARÇACIK SÜRÜ OPTİMİZASYONU İLE BIST SÜRDÜRÜLEBİLİRLİK 25 ENDEKSİ UYGULAMASI. PAUSBED. 2025(67):101-13.
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