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Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry

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  • Murat Çimen

    (Sakarya Uygulamalı Bilimler Üniversitesi)

Abstract

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.

Suggested Citation

  • Murat Çimen, 2024. "Portfolio Optimization with Artificial Hummingbird Algorithm for Cement Industry," Istanbul Business Research, Istanbul University Business School, vol. 53(3), pages 351-378, December.
    • Handle: RePEc:ist:ibsibr:v:53:y:2024:i:3:p:351-378
    DOI: 10.26650/ibr.2024.53.1250778
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    References listed on IDEAS

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    1. Ghada Atteia & Nagwan Abdel Samee & El-Sayed M. El-Kenawy & Abdelhameed Ibrahim, 2022. "CNN-Hyperparameter Optimization for Diabetic Maculopathy Diagnosis in Optical Coherence Tomography and Fundus Retinography," Mathematics, MDPI, vol. 10(18), pages 1-30, September.
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