Research Article


DOI :10.26650/ibr.2024.53.1250778   IUP :10.26650/ibr.2024.53.1250778    Full Text (PDF)

Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry

Murat Erhan Çimen

Portfolio optimization, which is performed while investing in any asset, is an important issue for all investors and finance researchers. In this study, the Artificial Hummingbird Optimization Algorithm (AHA), which has been proposed in recent years, was implemented for portfolio optimization by adapting it to Modern Portfolio Theory. Stocks have been selected as investment instruments in the portfolio. Stocks are classified as risky assets due to daily price fluctuations, depending on many natural or political events or decisions. In this study, since stocks are risky assets, the minimum risk criterion is preferred for a defensive investor. In addition, due to the Kahramanmaraş earthquake in Türkiye, this study aims to create a portfolio, especially within the cement sector, in a way that minimizes risk. With this objective in mind, as the originality of the study, AHA has been used to determine the optimal portfolio using stocks in the cement sector in BIST. Statistical analysis and the Wilcoxon test were conducted for the AHA results. Subsequently, several portfolios were determined based on the AHA’s statistical results. Furthermore, to measure the risk and return performance for each portfolio, total normalized returns, CAPM analysis, Sharpe Ratio, and Treynor ratio were calculated, and their results were compared to each other. The results show that Portfolio 6 exhibited the best performance in terms of the minimum risk criterion among the optimized portfolios using AHA.


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References

  • Abid, M. S., Apon, H. J., Morshed, K. A., & Ahmed, A. (2022). Optimal planning of multiple renewable energy-integrated distribution system with uncertainties using artificial hummingbird algorithm. IEEE Access, 10, 40716-40730. google scholar
  • Akgüç, O. (1998). Financial management [Finansal Yönetim]. Avcıol Basım Yayım. google scholar
  • Akgül, A., Karaca, Y., Pala MA, Çimen, M., Boz, A., & Yıldız, M. (2024). Chaos Theory, Advanced Me-taheuristic Algorithms and Their Newfangled Deep Learning Architecture Optimization Applications: A Review. Fractals, 32(3). google scholar
  • Akkaya, M. (2021). An Analysis of the stock market volatility spread in emerging countries. Istanbul Busi-ness Research, 20(2), 215-233. google scholar
  • Akyer, H., Kalaycı, C., & Aygören, H. (2018). Particle swarm optimization algorithm for mean-variance portfolio optimization: A case study of Istanbul Stock Exchange. Pamukkale University Journal of Engi-neering Sciences, 24(1), 124-129. google scholar
  • Arıöz, Ö., & Yıldırım, K. (2012). Türkiye’de çimento sektöründeki belirsizlikler ve Türk çimento sektörünün SWOT analizi [Uncertaıntıes in cement industry in turkey and Swot analysis Of Turkısh cement industry]. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 32. google scholar
  • Atik, A., & Kovacevic, I. (2022). Comparison of the companies on the BIST sustainability index with other listed companies in the context of earnings manipulation. Istanbul Business Research, 51(2). google scholar
  • Atteia, G., Abdel Samee, N., El-Kenawy, M., E. S., & Ibrahim, A. (2022). CNN-Hyperparameter optimi-zation for diabetic maculopathy diagnosis in optical coherence tomography and fundus retinography. Mathematics, 10(18), 3274. google scholar
  • Atukalp, M. E. (2019). Borsa İstanbul’da işlem gören çimento firmalarının finansal performansının analizi [Analysis of financial performance of cement firms traded on borsa İstanbul]. Muhasebe ve Finansman Dergisi, 81, 213-230. google scholar
  • Ayan, T., & Akay, A. (2014). Tahmine dayalı portföy optimizasyonu: Modern portföy teorisinde risk ve beklenen getiri kavramlarına alternatif bir yaklaşım [Portfolio optimisation based on forecasting: an alternati-ve approach to concepts of expected return and risk in modern portfolio theory]. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi,119-132. google scholar
  • Borndörfer, R., Grötschel, M., & Löbel, A. (1998). Optimization of transportation systems. google scholar
  • Çankal, A. (2015). Genetik algoritma kullanarak hisse senedi portföy optimizasyonu: BİST-30’da bir uygulama [Portfolio optimizaton with metaheuristic algorithms: Bist 30 application]. Finans Ekonomi ve Sosyal Araştırmalar Dergisi, 7(1), 164-176 google scholar
  • Cedersund, G., Samuelsson, O., Ball, G., Tegner, J., & Gomez-Cabrero, D. (2016). Optimization in biology parameter estimation and the associated optimization problem. Uncertainty in Biology: A Computational Modeling Approach, 177-197. google scholar
  • Çelengi, A. (2018). Potfolio optimization based on sharpe performance ratiowith artificial bee colony al-gorithm: BIST 30 application (Master Thesis). Retrieved from: https://tez.yok.gov.tr/UlusalTezMerkezi/ tezDetay.jsp?id=1QoGWRUCuyTL1zsKNrE0gw&no=xE6JBxq9FPMhxxdYqlB-hQ. google scholar
  • Çelenli, A. Z., Eğrioğlu, E., & Çorba, B. Ş. (2015). IMKB 30 indeksini oluşturan hisse senetleri için parçacık sürü optimizasyonu yöntemlerine dayalı portföy optimizasyonu [Particle swarm optimization methods nased on portfolio optimization for IMKB 30 stock shares]. Doğuş Üniversitesi Dergisi, 16(1), 25-33. google scholar
  • Çelik, M., & Tekşen, Ö. (2021). Does it matter how to fund?: A research on Turkish deposit banks. Istanbul Business Research, 50(2), 359-383. google scholar
  • Cembureau. (2021). Activity Report. Retrieved from https://cembureau.eu/media/03cgodyp/2021-activity-report.pdf google scholar
  • Chang, J. F., Wang, T. C., & Min, Y. T. (2010). Using genetic algorithms to construct a low-risk fund portfolio based on the Taiwan 50 Index. In In 2010 International Conference on Computational Aspects of Social Networks (pp. 284-289). google scholar
  • Chen, T., Chen, Y., He, Z., Li, E., Zhang, C., & Huang, Y. (2022). A novel marine predators algorithm with adaptive update strategy. He Journal of Supercomputing, 1-34. google scholar
  • Çimen, M. (2022). Hybrid and chaotic metaheuristic algorithms and design of model predictive control structures (Doctoral dissertation). Retrieved from: https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp ?id=HMCfRb6JuSHHUffB06C6TA&no=jd4x9Mc-mLvGvl0rQWJ87Q. google scholar
  • Çimen, M. E., Garip, Z., & Boz, A. F. (2021). Comparison of metaheuristic optimization algorithms with a new modified deb feasibility constraint handling technique. Turkish Journal of Electrical Engineering and Computer Sciences, 29(7), 3270-3289. google scholar
  • Çimen, M., Garip, Z., M, E., & Boz, A. (2022). Fuzzy Logic PID design using genetic algorithm under overshoot constrained conditions for heat exchanger Control. Journal of the Institute of Science and Tech-nology, 12(1), 164-181. google scholar
  • Demirdelen, T., Esenboga, B., Aksu, I. O., Ozdogan, A., Yavuzdeger, A., Ekinci, F., & Tümay, M. (2022). Modeling and experimental validation of dry-type transformers with multiobjective swarm intelligen-ce-based optimization algorithms for industrial application. Neural Computing and Applications, 34(2), 1079-1098. google scholar
  • Eberhart, R., & Kennedy, J. (1995). Particle swarm optimization. In Proceedings of the IEEE international conference on neural networks (pp. 1942-1948). google scholar
  • Elbannan, M. (2015). The capital asset pricing model: An overview of the theory. International Journal of Economics and Finance, 7(1). google scholar
  • Fennelly, B. A. (2012). Observations from the jewel rooms. Ecotone, 8(1), 74-85. google scholar
  • Sayılgan., G& Mut, A.D. (2010). Portföy Optimizasyonunda alt kısmi moment ve yarı-varyans ölçütlerinin kullanılması [Uses of variance and lower partial moment measures for portfolio optimization]. BDDK Bankacılık ve Finansal Piyasalar, 1(1), 47-73. google scholar
  • Garip, O. (2014). The optimal portfolio selection and study about trading in ISE (Master thesis). Retrieved from: https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp?id=GaAdssvkH7WzsdIt0SsomQ&no=xBF M3crEKuaBtBWRCdYijA. google scholar
  • Garip, Z., Karayel, D., & Çimen, ME. (2021). Global path planning in particle swarm optimization based mobile robots [Parçacık sürü optimizasyon tabanlı mobil robotlarda global yol planlama], Journal of Smart Systems Research 2(1), 18-26. google scholar
  • Gladwell, M. (2009). Outliers (Çizginin Dışındakiler)-Bazı insanlar neden daha başarılı olur? google scholar
  • Gök, R., & Tiwari, A. K. (2022). Analysis of the frequency-based relationship between ınflation expectations and gold returns in turkey. Istanbul Business Research, 51(2). google scholar
  • Heidari, A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: algorithm and applications. Future Generation Computer Systems, 97, 849-872. google scholar
  • Holland, J. (1975). Adaptation in natural and artificial systems. Ann Arbor: Michigan Press. google scholar
  • Holland, J. H. (1975). Adaptation. Progress in Theoretical Biology IV. Academic Press. Retrieved from https://doi.org/10.1016/B978-0-12-543104-0.50012-3 google scholar
  • Hu, T., Ji, Y., Fei, F., Zhu, M., Jin, T., Xue, P., & Zhang, N. (2022). Optimization of COVID-19 prevention and control with low building energy consumption. Building and Environment, 219, 109233. google scholar
  • Hüseyinov, İ., & Uluçay, S. (2019). Application of genetic and particle swarm optimization algorithms to portfolio optimization problem: borsa İstanbul and crypto money exchange. In 2019 4th International Conference on Computer Science and Engineering (UBMK). google scholar
  • Jin, N., & Rahmat-Samii, Y. (2008). Particle swarm optimization for antenna designs in engineering electro-magnetics. Journal of Artificial Evolution and Applications. google scholar
  • Karakul, A. K., & Özaydin, G. (2019). Topsis ve vikor yöntemleri ile finansal performans değerlendirmesi: xelkt üzerinde bir uygulama [Financial performance evaluation by using topsis and vikor methods: An Application On XELKT]. Dumlupınar Üniversitesi Sosyal Bilimler Dergisi, 60, 68-86. google scholar
  • Karan, M. (2001). Yatırım Analizi ve Portföy Yönetimi (Gazi Kitap). google scholar
  • Karcıoğlu, R., & Yalçın, S. (2022). Sezgisel bulanık topsis yöntemiyle portföy seçimi: borsa istanbul’da bir uygulama [Portfolio selection with ıntuinıstionic fuzyy topsis method: an application at borsa Istanbul]. Muhasebe ve Finansman Dergisi, 94, 151-184. google scholar
  • Kiyosaki, R. T., & Lechter, S. L. (2001). Rich dad’s cashflow quadrant: rich dad’s guide to financial freedom. Business Plus. google scholar
  • Koker, R. (2013). A genetic algorithm approach to a neural-network-based inverse kinematics solution of robotic manipulators based on error minimization. Information Sciences, 222, 528-543. google scholar
  • Leys, F., Reynaerts, D., & Vandepitte, D. (2016). Outperforming hummingbirds’ load-lifting capability with a lightweight hummingbird-like flapping-wing mechanism. Biology Open, 5(8), 1052-1060. google scholar
  • Markowitz, H. M. (1952). Portfolio selection. Markowitz, H. M., 7(1), 77-91. google scholar
  • Markowitz, H. M. (1959). Portfolio selection, rfficient diversification of investment (New York:). google scholar
  • Mercangöz, B. (2018). Portfoy optimization by using partical swarm algorithm: an ımplementation with transportation sector shares in borsa İstanbul 30 [Parçacık sürü optimizasyonu ile portföy optimizasyonu: borsa İstanbul ulaştırma sektörü hisseleri üzerine bir uygulama]. Special Issue on Applied Economics and Finance, 14, 126-136. google scholar
  • Mirjalili, S, & Lewis, A. (2016). The whale optimization algorithm. Advances in Engineering Software, 95, 51-67. google scholar
  • Mirjalili, Seyedali. (2015). Moth-flame optimization algorithm: A novel nature-inspired heuristic paradigm. Knowledge-Based Systems, 89, 228-249. google scholar
  • Mirjalili, Seyedali. (2016). Sca: a sine cosine Algorithm for solving optimization problems. Knowledge-Ba-sed Systems, 96, 120-133. Retrieved from https://doi.org/10.1016/j.knosys.2015.12.022 google scholar
  • Mirjalili, Seyedali. (2019). Genetic algorithm. Evolutionary Algorithms and Neural Networks, 43-55. google scholar
  • Moustafa, Y. (2007). Relationship between risk and return in portfolio management and capital asset pricing model (Master Thesis). Retrieved from: https://tez.yok.gov.tr/UlusalTezMerkezi/tezDetay.jsp?id=snlx0u xxWC8QyNjN7hnKpg&no=UAk2iR4NX6Rx5u4e5MRByA google scholar
  • Nocedal, J., & Wright, S. (2006). Numerical optimization. (Second Edition,Ed.). Springer. google scholar
  • Oh, K. J., Kim, T. Y., & Min, S. (2005). Using genetic algorithm to support portfolio optimization for index fund management. Expert Systems with Applications, 28(2), 371-379. google scholar
  • Rajagopal, K., Cimen, M. E., Jafari, S., Singh, J. P., Roy, B. K., Akmese, O. F., & Akgul, A. (2021). A fa-mily of circulant megastable chaotic oscillators, its application for the detection of a feeble signal and PID controller for time-delay systems by using chaotic SCA algorithm. Chaos, Solitons and Fractals, 148(May), 110992. Retrieved from https://doi.org/10.1016/j.chaos.2021.110992 google scholar
  • Ramadan, A., Ebeed, M., Kamel, S., Ahmed, E. M., & TostadoVeliz, M. (2023). Optimal allocation of rene-wable DGs using artificial hummingbird algorithm under uncertainty conditions. Ain Shams Engineering Journal, 14(2), 101872. google scholar
  • Ramadan, A., Kamel, S., Hassan, M. H., Ahmed, E. M., & Hasanien, H. M. (2022). Accurate Photovoltaic Models Based on an Adaptive Opposition Artificial Hummingbird Algorithm. Electronics, 11(3), 318. google scholar
  • Ramshe, M., Gharakhani, M., Feyz, A., & Sadjadi, S. J. (2021). A firefly algorithm for portfolio optimization problem with cardinality constraint. International Journal of Industrial Engineering and Management Science, 8(1), 24-33. google scholar
  • Salko, R. K., Schmidt, R. C., & Avramova, M. N. (2015). Optimization and parallelization of the thermal-hydraulic subchannel code CTF for high-fidelity multi-physics applications. Annals of Nuclear Energ, 84, 122-130. google scholar
  • Sedighi, M., Jahangirnia, H., & Gharakhani, M. (2018). portfolio multi-objective optimization and its perfor-mance evaluation by quantile-based risk measures. In 2nd International Conference on Modern Develop-ments in Management, Economics and Accounting. Tahran. google scholar
  • Sedighi, M., Jahangirnia, H., & Gharakhani, M. (2019). A new efficient metaheuristic model for stock portfo-lio management and its performance evaluation by risk-adjusted methods. International Journal of Finan-ce & Managerial AccountingInternational Journal of Finance & Managerial Accounting, 3(12), 63-77. google scholar
  • Tobalske, B. W., Warrick, D. R., Clark, C. J., Powers, D. R., Hedrick, T. L., Hyder, G. A., & Biewener, A. A. (2007). Three-dimensional kinematics of hummingbird flight. Journal of Experimental Biology, 210(13), 2368-2382. google scholar
  • Ulucan, A. (2004). Portföy Optimizasyonu [Portfolio Optimization]. Ankara, Siyasal Kitabevi. google scholar
  • Xing-Shi He, Qin-Wei Fan, Mehmet Karamanoglu, and X.-S. Y. (2019). Comparison of constraint-handling techniques for metaheuristic optimization. International Conference Computatio, 11538(May), 648-657. Retrieved from https://doi.org/10.1007/978-3-030-22744-9 google scholar
  • Yalcin, N. (2022). Content analysis of audit reports in stock ındices euronext 100 vs bist 100. Istanbul Busi-ness Research, 51(2). google scholar
  • Yang, X. S. (2009). Firefly algorithms for multimodal optimization. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5792 LNCS, 169-178. Retrieved from https://doi.org/10.1007/978-3-642-04944-6_14 google scholar
  • Yang, X. S., & Deb, S. (2009). Cuckoo search via Levy flights. 2009 World Congress on Nature andBiologi-cally Inspired Computing, NABIC 2009 - Proceedings, 210-214. Retrieved from https://doi.org/10.1109/ NABIC.2009.5393690 google scholar
  • Yang, X.-S. (2020). Nature-inspired optimization algorithms. Academic Press. google scholar
  • Yücel, Ö. (2016). Bist endekslerinin risk temelli performans karşılaştırması [Risk based performance com-parison of bist ındices]. İşletme ve İktisat Çalışmaları Dergisi, 4(4), 151-164. google scholar
  • Zhao, W., Wang, L., & Mirjalili, S. (2022). Artificial hummingbird algorithm: A new bio-inspired optimi-zer with its engineering applications. Computer Methods in Applied Mechanics and Engineering, 388, 114194. google scholar

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APA

Çimen, M.E. (2025). Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry. Istanbul Business Research, 53(3), 351-378. https://doi.org/10.26650/ibr.2024.53.1250778


AMA

Çimen M E. Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry. Istanbul Business Research. 2025;53(3):351-378. https://doi.org/10.26650/ibr.2024.53.1250778


ABNT

Çimen, M.E. Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry. Istanbul Business Research, [Publisher Location], v. 53, n. 3, p. 351-378, 2025.


Chicago: Author-Date Style

Çimen, Murat Erhan,. 2025. “Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry.” Istanbul Business Research 53, no. 3: 351-378. https://doi.org/10.26650/ibr.2024.53.1250778


Chicago: Humanities Style

Çimen, Murat Erhan,. Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry.” Istanbul Business Research 53, no. 3 (Jan. 2025): 351-378. https://doi.org/10.26650/ibr.2024.53.1250778


Harvard: Australian Style

Çimen, ME 2025, 'Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry', Istanbul Business Research, vol. 53, no. 3, pp. 351-378, viewed 19 Jan. 2025, https://doi.org/10.26650/ibr.2024.53.1250778


Harvard: Author-Date Style

Çimen, M.E. (2025) ‘Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry’, Istanbul Business Research, 53(3), pp. 351-378. https://doi.org/10.26650/ibr.2024.53.1250778 (19 Jan. 2025).


MLA

Çimen, Murat Erhan,. Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry.” Istanbul Business Research, vol. 53, no. 3, 2025, pp. 351-378. [Database Container], https://doi.org/10.26650/ibr.2024.53.1250778


Vancouver

Çimen ME. Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry. Istanbul Business Research [Internet]. 19 Jan. 2025 [cited 19 Jan. 2025];53(3):351-378. Available from: https://doi.org/10.26650/ibr.2024.53.1250778 doi: 10.26650/ibr.2024.53.1250778


ISNAD

Çimen, MuratErhan. Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry”. Istanbul Business Research 53/3 (Jan. 2025): 351-378. https://doi.org/10.26650/ibr.2024.53.1250778



TIMELINE


Submitted13.02.2023
Accepted23.12.2024
Published Online09.01.2025

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