Research Article
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Year 2023, Volume: 12 Issue: 2, 557 - 565, 27.04.2023

Salih Serkan Kaleli

https://doi.org/10.33206/mjss.1215054

Abstract

Keywords

Genetik Algoritma Optimizasyon Optimum Portföy BİST-30 İşletme Yönetimi

References

  • Bey, K. B., Belgacem, A. ve Nacer, H. (2018). A new task scheduling approach based on Spacing Multi- Objective Genetic algorithm in cloud. Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems, 17, 189–195. https://doi.org/10.15439/2018f180
  • Beybur, M. (2021). Covıd-19 Pandemisinin Türk Bankacılık Sektörü Kredileri Öz Effects Of The Covıd-19 Pandemıc On Turkısh Bankıng Sector Loans And Npls Abstract GİRİŞ İlk olarak 2019 Aralık ayında ortaya çıkan ve 2020 yılı Mart ayında Türkiye ’ de de görülen Covid-19 pandem. 28, 181–210.
  • Chen, J. S. ve Hou, J. L. (2006, June). A combination genetic algorithm with applications on portfolio optimization. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 197-206). Springer, Berlin, Heidelberg.
  • Chen, W., Zhang, H., Mehlawat, M. K. ve Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943. https://doi.org/10.1016/j.asoc.2020.106943
  • Chou, Y. H., Kuo, S. Y. ve Lo, Y. T. (2017). Portfolio optimization based on funds standardization and genetic algorithm. IEEE Access, 5, 21885–21900. https://doi.org/10.1109/ACCESS.2017.2756842
  • Ergün, T. ve Üçoğlu, D. (2022). COVID-19 Pandemisi’nin Tekstil, Deri ve Giyim Eşyası Sektörlerinde Faaliyet Gösteren Firmalara ve Finansal Raporlarına Etkileri: BIST’te Bir Araştırma. Muhasebe Enstitüsü Dergisi / Journal of Accounting Institute, 0(66), 95–112. https://doi.org/10.26650/med.998932
  • Fernandez, E., Navarro, J., Solares, E. ve Coello, C. C. (2019). A novel approach to select the best portfolio considering the preferences of the decision maker. Swarm and Evolutionary Computation, 46(November 2018), 140–153. https://doi.org/10.1016/j.swevo.2019.02.002
  • Gümüş, A. ve Bilgi, M. (2020). Covid - 19 Salgın Hastalığının Borsaya Et Kisi : Turizm Ve Ulaştırma Endeksleri Üzerine Bir Uygulama The Effect Of Covid - 19 Epidemic On The Stock Market : An Application On Tourism And Transport Indices Özet Gümüş , A . & Hacıevliyagil , N ./ Covid 19 S. 76–98.
  • Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A. ve Prasath, V. B. S. (2019). Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Information (Switzerland), 10(12). https://doi.org/10.3390/info10120390
  • Keskintürk, T. (2007). İyi çeşitlendirilmiş portföy büyüklüğünün genetik algoritma tekniği kullanılarak incelenmesi. Yönetim, 56, 78-90
  • Li, Y., Wang, S., Hong, X. ve Li, Y. (2018). Multi-objective task scheduling optimization in cloud computing based on genetic algorithm and differential evolution algorithm. Chinese Control Conference, CCC, 2018-July, 4489– 4494. https://doi.org/10.23919/ChiCC.2018.8483505
  • Lin, C.-M. (2007). An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Applied Mathematical Sciences, 1(5), 201–210.
  • Metawa, N., Elhoseny, M., Hassan, M. K. ve Hassanien, A. E. (2017). Loan portfolio optimization using genetic algorithm: A case of credit constraints. 2016 12th International Computer Engineering Conference, ICENCO 2016: Boundless Smart Societies, 59–64. https://doi.org/10.1109/ICENCO.2016.7856446
  • Pavlenko, A. A., Kukartsev, V. V., Tynchenko, V. S., Mikhalev, A. S., Chzhan, E. A. ve Lozitskaya, E. V. (2019). Optimal parameters selection of the genetic algorithm for global optimization. Journal of Physics: Conference Series, 1353(1), 0–5. https://doi.org/10.1088/1742-6596/1353/1/012105
  • Sinha, P., Chandwani, A. ve Sinha, T. (2015). Algorithm of construction of optimum portfolio of stocks using genetic algorithm. International Journal of Systems Assurance Engineering and Management, 6(4), 447–465. https://doi.org/10.1007/s13198-014-0293-7
  • Soldatos, J. ve Kyriazis, D. (2022). Big data and artificial ıntelligence in digital finance: Increasing personalization and trust in digital finance using big data and AI.
  • Ta, V. D., Liu, C. M. ve Tadesse, D. A. (2020). Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 10(2), 437.
  • Vasiani, V. D., Handari, B. D. ve Hertono, G. F. (2020). Stock portfolio optimization using priority index and genetic algorithm. In Journal of physics: conference series (Vol. 1442, No. 1, p. 012031). IOP Publishing.
  • Venturelli, D. ve Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1–2), 17–30. https://doi.org/10.1007/s42484-019-00001-w
  • Yakut, E. ve Çankal, A. (2016). Çok amaçlı genetik algoritma ve hedef programlama metotlarını kullanarak hisse senedi portföy optimizasyonu: BIST-30'da Bir uygulama. Business and Economics Research Journal, 7(2), 43.
  • Yaman, I. ve Erbay Dalkılıç, T. (2021). A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm. Expert Systems with Applications, 169. https://doi.org/10.1016/j.eswa.2020.114517
  • Yang, X. (2006). Improving portfolio efficiency: A genetic algorithm approach. Computational Economics, 28(1), 1–14. https://doi.org/10.1007/s10614-006-9021-y
  • Yurdakul, O. ve Yavuz, B. (2021). Çoklu Kaynak gerektiren parçalarda kaynak sırasının genetik algoritma kullanılarak belirlenmesi. European Journal of Science and Technology, 28, 990–992. https://doi.org/10.31590/ejosat.1012352

Analysis of Bist-30 Companies' Pre-Pandemic Sales Data with Genetic Algorithm and Creating an Optimum Portfolio

Year 2023, Volume: 12 Issue: 2, 557 - 565, 27.04.2023

Salih Serkan Kaleli

https://doi.org/10.33206/mjss.1215054

Abstract

Portfolio Optimization problem (PO) is one of the problems that cannot be solved by classical methods in which the best portfolio is selected for investors. The purpose of portfolio optimization is to select the stock with the lowest risk, which will generate the highest return.. In this study, it is aimed to select the most suitable stock in the system designed by using genetic algorithm by transferring the 5-year sales data (60 sales data) between 2016 December and 2021 December obtained from Bist-30 companies to the MATLAB platform. The main difference in the study is that the 5-year data of the companies were analyzed separately in 3 groups as 1-year, 3-year and 5-year, and comparative results were given according to user-defined risk values. The proposed method obtained the most efficient result for the risk coefficient of 0.20. For this coefficient value, it has been determined that 10, 14 and 15 stocks will be selected in 3 groups, respectively. In addition, in this study, the changes in the sales values of the companies by years were evaluated by considering the current market conditions and pandemic conditions.

Keywords

Genetic Algorithm Optimization Optimum Portfolio BIST-30 Business Management

References

  • Bey, K. B., Belgacem, A. ve Nacer, H. (2018). A new task scheduling approach based on Spacing Multi- Objective Genetic algorithm in cloud. Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems, 17, 189–195. https://doi.org/10.15439/2018f180
  • Beybur, M. (2021). Covıd-19 Pandemisinin Türk Bankacılık Sektörü Kredileri Öz Effects Of The Covıd-19 Pandemıc On Turkısh Bankıng Sector Loans And Npls Abstract GİRİŞ İlk olarak 2019 Aralık ayında ortaya çıkan ve 2020 yılı Mart ayında Türkiye ’ de de görülen Covid-19 pandem. 28, 181–210.
  • Chen, J. S. ve Hou, J. L. (2006, June). A combination genetic algorithm with applications on portfolio optimization. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 197-206). Springer, Berlin, Heidelberg.
  • Chen, W., Zhang, H., Mehlawat, M. K. ve Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943. https://doi.org/10.1016/j.asoc.2020.106943
  • Chou, Y. H., Kuo, S. Y. ve Lo, Y. T. (2017). Portfolio optimization based on funds standardization and genetic algorithm. IEEE Access, 5, 21885–21900. https://doi.org/10.1109/ACCESS.2017.2756842
  • Ergün, T. ve Üçoğlu, D. (2022). COVID-19 Pandemisi’nin Tekstil, Deri ve Giyim Eşyası Sektörlerinde Faaliyet Gösteren Firmalara ve Finansal Raporlarına Etkileri: BIST’te Bir Araştırma. Muhasebe Enstitüsü Dergisi / Journal of Accounting Institute, 0(66), 95–112. https://doi.org/10.26650/med.998932
  • Fernandez, E., Navarro, J., Solares, E. ve Coello, C. C. (2019). A novel approach to select the best portfolio considering the preferences of the decision maker. Swarm and Evolutionary Computation, 46(November 2018), 140–153. https://doi.org/10.1016/j.swevo.2019.02.002
  • Gümüş, A. ve Bilgi, M. (2020). Covid - 19 Salgın Hastalığının Borsaya Et Kisi : Turizm Ve Ulaştırma Endeksleri Üzerine Bir Uygulama The Effect Of Covid - 19 Epidemic On The Stock Market : An Application On Tourism And Transport Indices Özet Gümüş , A . & Hacıevliyagil , N ./ Covid 19 S. 76–98.
  • Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A. ve Prasath, V. B. S. (2019). Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Information (Switzerland), 10(12). https://doi.org/10.3390/info10120390
  • Keskintürk, T. (2007). İyi çeşitlendirilmiş portföy büyüklüğünün genetik algoritma tekniği kullanılarak incelenmesi. Yönetim, 56, 78-90
  • Li, Y., Wang, S., Hong, X. ve Li, Y. (2018). Multi-objective task scheduling optimization in cloud computing based on genetic algorithm and differential evolution algorithm. Chinese Control Conference, CCC, 2018-July, 4489– 4494. https://doi.org/10.23919/ChiCC.2018.8483505
  • Lin, C.-M. (2007). An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Applied Mathematical Sciences, 1(5), 201–210.
  • Metawa, N., Elhoseny, M., Hassan, M. K. ve Hassanien, A. E. (2017). Loan portfolio optimization using genetic algorithm: A case of credit constraints. 2016 12th International Computer Engineering Conference, ICENCO 2016: Boundless Smart Societies, 59–64. https://doi.org/10.1109/ICENCO.2016.7856446
  • Pavlenko, A. A., Kukartsev, V. V., Tynchenko, V. S., Mikhalev, A. S., Chzhan, E. A. ve Lozitskaya, E. V. (2019). Optimal parameters selection of the genetic algorithm for global optimization. Journal of Physics: Conference Series, 1353(1), 0–5. https://doi.org/10.1088/1742-6596/1353/1/012105
  • Sinha, P., Chandwani, A. ve Sinha, T. (2015). Algorithm of construction of optimum portfolio of stocks using genetic algorithm. International Journal of Systems Assurance Engineering and Management, 6(4), 447–465. https://doi.org/10.1007/s13198-014-0293-7
  • Soldatos, J. ve Kyriazis, D. (2022). Big data and artificial ıntelligence in digital finance: Increasing personalization and trust in digital finance using big data and AI.
  • Ta, V. D., Liu, C. M. ve Tadesse, D. A. (2020). Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 10(2), 437.
  • Vasiani, V. D., Handari, B. D. ve Hertono, G. F. (2020). Stock portfolio optimization using priority index and genetic algorithm. In Journal of physics: conference series (Vol. 1442, No. 1, p. 012031). IOP Publishing.
  • Venturelli, D. ve Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1–2), 17–30. https://doi.org/10.1007/s42484-019-00001-w
  • Yakut, E. ve Çankal, A. (2016). Çok amaçlı genetik algoritma ve hedef programlama metotlarını kullanarak hisse senedi portföy optimizasyonu: BIST-30'da Bir uygulama. Business and Economics Research Journal, 7(2), 43.
  • Yaman, I. ve Erbay Dalkılıç, T. (2021). A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm. Expert Systems with Applications, 169. https://doi.org/10.1016/j.eswa.2020.114517
  • Yang, X. (2006). Improving portfolio efficiency: A genetic algorithm approach. Computational Economics, 28(1), 1–14. https://doi.org/10.1007/s10614-006-9021-y
  • Yurdakul, O. ve Yavuz, B. (2021). Çoklu Kaynak gerektiren parçalarda kaynak sırasının genetik algoritma kullanılarak belirlenmesi. European Journal of Science and Technology, 28, 990–992. https://doi.org/10.31590/ejosat.1012352

Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma

Year 2023, Volume: 12 Issue: 2, 557 - 565, 27.04.2023

Salih Serkan Kaleli

https://doi.org/10.33206/mjss.1215054

Abstract

Portföy Optimizasyonu problemi (PO), yatırımcılar için en iyi portföyün seçildiği çözülmesi klasik yöntemlerle mümkün olmayan problemlerden birisidir. Portföy optimizasyonundaki amaç, en yüksek getiriyi elde edecek olan hisse senedinin en düşük riskle seçilmesidir. Klasik yöntemler kesin bir çözüm bulamadığında, sezgisel teknikler yaklaşık bir çözüm bulmak için tasarlanmaktadır. Literatürde portföy optimizasyonu probleminin çözümü için çok fazla sezgisel teknikler kullanılmış ve başarılı sonuçlar elde edilmiştir. Bu çalışmada Bist-30 şirketlerinden elde edilen 2016 Aralık- 2021 Aralık arasındaki 5 yıllık satış verileri (60 adet satış verisi), MATLAB platformuna aktarılarak genetik algoritma kullanılıp tasarlanan sistemde en uygun hisse senedinin seçilmesi amaçlanmıştır. Çalışmadaki temel farklılık, şirketlerin 5 yıllık verileri, kendi içerisinde 1 yıllık, 3 yıllık, 5 yıllık olmak üzere 3 grupta ayrı ayrı incelenmiş olup kullanıcı tanımlı risk değerlerine göre karşılaştırılmalı sonuçlara yer verilmiştir. Önerilen yöntem en verimli sonucu, 0.20 risk katsayısı için elde etmiştir. Bu katsayı değeri için 3 grupta sırasıyla 10, 14 ve 15 adet hisse senedinin seçileceği tespit edilmiştir. Ek olarak, bu çalışmada şirketlerin yıllara göre satış değerlerindeki değişimler mevcut piyasa şartları ve pandemi koşulları göz önüne alınarak değerlendirilmiştir.

Keywords

Genetik Algoritma Optimizasyon Optimum Portföy BİST-30 İşletme Yönetimi

References

  • Bey, K. B., Belgacem, A. ve Nacer, H. (2018). A new task scheduling approach based on Spacing Multi- Objective Genetic algorithm in cloud. Communication Papers of the 2018 Federated Conference on Computer Science and Information Systems, 17, 189–195. https://doi.org/10.15439/2018f180
  • Beybur, M. (2021). Covıd-19 Pandemisinin Türk Bankacılık Sektörü Kredileri Öz Effects Of The Covıd-19 Pandemıc On Turkısh Bankıng Sector Loans And Npls Abstract GİRİŞ İlk olarak 2019 Aralık ayında ortaya çıkan ve 2020 yılı Mart ayında Türkiye ’ de de görülen Covid-19 pandem. 28, 181–210.
  • Chen, J. S. ve Hou, J. L. (2006, June). A combination genetic algorithm with applications on portfolio optimization. In International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems (pp. 197-206). Springer, Berlin, Heidelberg.
  • Chen, W., Zhang, H., Mehlawat, M. K. ve Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943. https://doi.org/10.1016/j.asoc.2020.106943
  • Chou, Y. H., Kuo, S. Y. ve Lo, Y. T. (2017). Portfolio optimization based on funds standardization and genetic algorithm. IEEE Access, 5, 21885–21900. https://doi.org/10.1109/ACCESS.2017.2756842
  • Ergün, T. ve Üçoğlu, D. (2022). COVID-19 Pandemisi’nin Tekstil, Deri ve Giyim Eşyası Sektörlerinde Faaliyet Gösteren Firmalara ve Finansal Raporlarına Etkileri: BIST’te Bir Araştırma. Muhasebe Enstitüsü Dergisi / Journal of Accounting Institute, 0(66), 95–112. https://doi.org/10.26650/med.998932
  • Fernandez, E., Navarro, J., Solares, E. ve Coello, C. C. (2019). A novel approach to select the best portfolio considering the preferences of the decision maker. Swarm and Evolutionary Computation, 46(November 2018), 140–153. https://doi.org/10.1016/j.swevo.2019.02.002
  • Gümüş, A. ve Bilgi, M. (2020). Covid - 19 Salgın Hastalığının Borsaya Et Kisi : Turizm Ve Ulaştırma Endeksleri Üzerine Bir Uygulama The Effect Of Covid - 19 Epidemic On The Stock Market : An Application On Tourism And Transport Indices Özet Gümüş , A . & Hacıevliyagil , N ./ Covid 19 S. 76–98.
  • Hassanat, A., Almohammadi, K., Alkafaween, E., Abunawas, E., Hammouri, A. ve Prasath, V. B. S. (2019). Choosing mutation and crossover ratios for genetic algorithms-a review with a new dynamic approach. Information (Switzerland), 10(12). https://doi.org/10.3390/info10120390
  • Keskintürk, T. (2007). İyi çeşitlendirilmiş portföy büyüklüğünün genetik algoritma tekniği kullanılarak incelenmesi. Yönetim, 56, 78-90
  • Li, Y., Wang, S., Hong, X. ve Li, Y. (2018). Multi-objective task scheduling optimization in cloud computing based on genetic algorithm and differential evolution algorithm. Chinese Control Conference, CCC, 2018-July, 4489– 4494. https://doi.org/10.23919/ChiCC.2018.8483505
  • Lin, C.-M. (2007). An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Applied Mathematical Sciences, 1(5), 201–210.
  • Metawa, N., Elhoseny, M., Hassan, M. K. ve Hassanien, A. E. (2017). Loan portfolio optimization using genetic algorithm: A case of credit constraints. 2016 12th International Computer Engineering Conference, ICENCO 2016: Boundless Smart Societies, 59–64. https://doi.org/10.1109/ICENCO.2016.7856446
  • Pavlenko, A. A., Kukartsev, V. V., Tynchenko, V. S., Mikhalev, A. S., Chzhan, E. A. ve Lozitskaya, E. V. (2019). Optimal parameters selection of the genetic algorithm for global optimization. Journal of Physics: Conference Series, 1353(1), 0–5. https://doi.org/10.1088/1742-6596/1353/1/012105
  • Sinha, P., Chandwani, A. ve Sinha, T. (2015). Algorithm of construction of optimum portfolio of stocks using genetic algorithm. International Journal of Systems Assurance Engineering and Management, 6(4), 447–465. https://doi.org/10.1007/s13198-014-0293-7
  • Soldatos, J. ve Kyriazis, D. (2022). Big data and artificial ıntelligence in digital finance: Increasing personalization and trust in digital finance using big data and AI.
  • Ta, V. D., Liu, C. M. ve Tadesse, D. A. (2020). Portfolio optimization-based stock prediction using long-short term memory network in quantitative trading. Applied Sciences, 10(2), 437.
  • Vasiani, V. D., Handari, B. D. ve Hertono, G. F. (2020). Stock portfolio optimization using priority index and genetic algorithm. In Journal of physics: conference series (Vol. 1442, No. 1, p. 012031). IOP Publishing.
  • Venturelli, D. ve Kondratyev, A. (2019). Reverse quantum annealing approach to portfolio optimization problems. Quantum Machine Intelligence, 1(1–2), 17–30. https://doi.org/10.1007/s42484-019-00001-w
  • Yakut, E. ve Çankal, A. (2016). Çok amaçlı genetik algoritma ve hedef programlama metotlarını kullanarak hisse senedi portföy optimizasyonu: BIST-30'da Bir uygulama. Business and Economics Research Journal, 7(2), 43.
  • Yaman, I. ve Erbay Dalkılıç, T. (2021). A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm. Expert Systems with Applications, 169. https://doi.org/10.1016/j.eswa.2020.114517
  • Yang, X. (2006). Improving portfolio efficiency: A genetic algorithm approach. Computational Economics, 28(1), 1–14. https://doi.org/10.1007/s10614-006-9021-y
  • Yurdakul, O. ve Yavuz, B. (2021). Çoklu Kaynak gerektiren parçalarda kaynak sırasının genetik algoritma kullanılarak belirlenmesi. European Journal of Science and Technology, 28, 990–992. https://doi.org/10.31590/ejosat.1012352
There are 23 citations in total.

Details

Primary Language Turkish
Journal Section Research Article
Authors

Salih Serkan Kaleli 0000-0003-2196-6050

Publication Date April 27, 2023
Submission Date December 6, 2022
Published in Issue Year 2023 Volume: 12 Issue: 2

Cite

APA Kaleli, S. S. (2023). Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma. MANAS Sosyal Araştırmalar Dergisi, 12(2), 557-565. https://doi.org/10.33206/mjss.1215054
AMA Kaleli SS. Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma. MJSS. April 2023;12(2):557-565. doi:10.33206/mjss.1215054
Chicago Kaleli, Salih Serkan. “Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma Ile Analizi Ve Optimum Portföy Oluşturma”. MANAS Sosyal Araştırmalar Dergisi 12, no. 2 (April 2023): 557-65. https://doi.org/10.33206/mjss.1215054.
EndNote Kaleli SS (April 1, 2023) Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma. MANAS Sosyal Araştırmalar Dergisi 12 2 557–565.
IEEE S. S. Kaleli, “Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma”, MJSS, vol. 12, no. 2, pp. 557–565, 2023, doi: 10.33206/mjss.1215054.
ISNAD Kaleli, Salih Serkan. “Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma Ile Analizi Ve Optimum Portföy Oluşturma”. MANAS Sosyal Araştırmalar Dergisi 12/2 (April 2023), 557-565. https://doi.org/10.33206/mjss.1215054.
JAMA Kaleli SS. Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma. MJSS. 2023;12:557–565.
MLA Kaleli, Salih Serkan. “Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma Ile Analizi Ve Optimum Portföy Oluşturma”. MANAS Sosyal Araştırmalar Dergisi, vol. 12, no. 2, 2023, pp. 557-65, doi:10.33206/mjss.1215054.
Vancouver Kaleli SS. Bist-30 Şirketlerinin Pandemi Öncesi-Sonrası Satış Verilerinin Genetik Algoritma ile Analizi ve Optimum Portföy Oluşturma. MJSS. 2023;12(2):557-65.
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