The impact of meta-heuristic hybrid algorithm analysis on portfolio diversification and excess return of investment funds and its role in Islamic financial marketing

Subject Areas : Economic and financial markets

Narges Salehi Azari ¹ , Shadi Shahverdiani ² , Gholamreza Zomorodian ³

1 - PhD student in financial engineering, Department of Financial Engineering, Quds City Branch, Islamic Azad University, Tehran, Iran.
2 - Assistant Professor, Department of Financial Management, Quds City Branch, Islamic Azad University, Tehran, Iran.
3 - Assistant Professor, Business Management Department, Central Tehran Branch, Islamic Azad University, Tehran, Iran.

Received: 2023-11-22 Accepted : 2024-01-26 Published : 2024-09-22

Keywords: Diversification, Portfolio, Hybrid algorithm, meta-heuristic, excess return,

Abstract :

The purpose of this research is to investigate the impact of meta-heuristic hybrid algorithm analysis on portfolio diversification and excess returns of investment funds. In terms of method, the current research is a part of correlation research. In correlation research, the researcher's effort is focused on discovering or determining the relationship between one or more variables. In fact, the purpose of this method is to study the limits of changes of one or more variables with the limits of changes of one or more variables, and from the point of view of the purpose of this research, it is an applied research, the results of which can be useful for shareholders, stock exchange officials, and researchers. It is useful and in terms of the type of post-event studies that examines hypotheses based on past financial data. The statistical population of this research includes all the companies admitted to the Tehran Stock Exchange during the period of 36 months in the period from April 2019 to March 2011, which number is 591 companies based on the Rahevard software. According to the conditions and application of the aforementioned restrictions, 150 companies were selected as a sample in the 36 months ending in March 1401. By observing the results of the stock portfolio selection models with single and combined measures, we find that in all three models, the amount of risk increases with the increase in return. This shows that investors, in order to obtain more return, They are forced to accept

References:

ابراهیمی، ایلناز و همتی، مریم،1397،نقش و اثرگذاری صندوق های بازنشستگی در ثبات مالی اقتصاد ایران؛لزوم شکل گیری مقام ناظر احتیاطی،بیست ‌و هشتمین همایش سالانه سیاست‌های پولی و ارزی :با موضوع: اصلاحات ساختاری برای ثبات مالی،تهران،
بحری ثالث، جمال؛ پاک مرام، عسگر؛ ولی زاده، مصطفی (1397). انتخاب و بهینه‌سازی سبد سهام با استفاده از روش میانگین واریانس مارکویتز با بهره‌گیری از الگوریتم‌های مختلف. دانش مالی تحلیل اوراق بهادار، دوره 11، شماره 37، صص 43-53.
بهزادپور، سمیرا و رحمانی، علی،1398،ارزیابی سودمندی اطلاعات ارائه شده در صورت های مالی صندوق های بازنشستگی
بیات علی؛ اسدی، لیدا. (1396). بهینه سازی پرتفوی سهام: سودمندی الگوریتم پرندگان و مدل مارکویتز. مهندسی مالی و مدیریت اوراق بهادار، دوره 8، شماره 32، صص 63-85.
پاک مرام، عسگر؛ بحری ثالث، جمال؛ ولی زاده، مصطفی (1396). انتخاب و بهينه سازي سبد سهام با استفاده از الگوريتم ژنتيك، با بهره گيري از مدل ميانگين-نيمه واريانس ماركويتز. مهندسی مالی و مدیریت اوراق بهادار، دوره 8، شماره 31، صص 19-42.
رضوی، سیدمهدی و بزرگ اصل، موسی و امیری، میثم و ابراهیمی سروعلیا، محمدحسن و خاشعی ورنامخواستی، وحید،1400،تخصیص دارایی صندوق های بازنشستگی با استفاده از رویکرد ترکیبی برنامه ریزی سناریو مبنا و روش بهترین-بدترین
شمس قارنه، ناصر؛ شهلایی، شهاب الدین (1398). بهینه¬سازی تنوع سهم¬های موجود در سبد صندوقهای سرمایه¬گذاری بخشی. فصلنامه علمی- پژوهشی مدیریت دارایی و تأمین مالی، سال 3، شماره 2، صص 1-14.
Cauwet, M.L., Liu, J., Rozière, B., Teytaud, O. Algorithm portfolios for noisy optimization. Annals of Mathematics and Artificial Intelligence, February 2016, Volume 76, Issue 1, pp 143–172.
Cui, X., Gao, J., Shi, Y., Zhu, SH. (2019). Time-consistent and self-coordination strategies for multi-period mean-Conditional Value-at-Risk portfolio selection . European Journal of Operational Research 276 (2019) 781–789 .
Ding, D., C.Sickles, R. (2018). Frontier efficiency, capital structure, and portfolio risk: An empirical analysis of U.S. banks. BRQ Business Research Quarterly, Volume 21, Issue 4, October–December 2018, Pages 262-277.
Doering, J, Kizys, R, Juan, AA, Fitó, À & Polat, O 2019, 'Metaheuristics for rich portfolio optimisation and risk management: current state and future trends', Operations Research Perspectives, vol. 6, 100121. https://doi.org/10.1016/j.orp.2019.100121
Fitzpatrick, Brian D. Church, Joshua. Hasse, Christopher H. (2012). Specialty Funds vs. General Mutual Funds and Socially Responsible Investment (SRI) Funds: An Intriguing Risk / Return Paradigm. Journal of Applied Business and Economics
Hu, Jin-Li. Chang, Tzu-Pu. Chou, Ray Yeutien. (2014). Market Conditions and the Effect of Diversification on Mutual Fund Performance: Should Funds Be More Concentrative Under Crisis? Journal of Productivity Analysis. 41. Issue 1. 141-151.
Huang, CH-Y., Chiou, CH-CH., Wu, T-H., Yang, SH-CH (2015), An integrated DEA-MODM methodology for portfolio optimization, Operational Research, Volume 15, Issue 1, pp 115–136.
Huang, J.-J. And So, L.-C. (2018) Application of Copula-GARCH to Estimate VaR of a Portfolio with Credit Default Swaps. Journal of M a thematical Finance, 8, 382-407.
Larsen, Glen A. Jr Bruce G Resnick. (2012). An Optimization Strategy for Enhancing the Performance of Fund-ofFunds Portfolios. Journal of Portfolio Management. 38. 2. 147-154
Lee, Yongjae & Kim, Woo Chang & Kim, Jang. (2020). Achieving Portfolio Diversification for Individuals with Low Financial Sustainability. Sustainability. 12. 7073. 10.3390/su12177073.
Liesiö, Juuso & Xu, Peng & Kuosmanen, Timo. (2020). Portfolio Diversification based on Stochastic Dominance under Incomplete Probability Information. European Journal of Operational Research. 286. 10.1016/j.ejor.2020.03.042.
Petropoulos, Anastasios & Chatzis, Sotirios & Siakoulis, Vasilis & Vlachogiannakis, Nikos. (2017). A Stacked Generalization System for Automated FOREX Portfolio Trading. Expert Systems with Applications. 90. 10.1016/j.eswa.2017.08.011.
Ta, & Liu, & Tadesse,. (2020). Portfolio Optimization-Based Stock Prediction Using Long-Short Term Memory Network in Quantitative Trading. Applied Sciences. 10. 437. 10.3390/app10020437.
Talat A. Rauf, A. (2009). Performance Evaluation of Pakistani Mutual Funds. Pakistan Economic and Social Review. 47. 199-214.
Yaman, I. and Dalkılıç, T.E., 2021. A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm. Expert Systems with Applications, 169, p.114517.
Zhai, Q. & Ye, T. & Huang, M. & Feng, S. & Li, H.. (2020). Whale Optimization Algorithm for Multiconstraint Second-Order Stochastic Dominance Portfolio Optimization. Computational Intelligence and Neuroscience. 2020. 1-19. 10.1155/2020/8834162.
Zoia, M. G., Biffi, P., & Nicolussi, F. (2018). Value at Risk and Expected Shortfall based on Gram-Charlier-like expansions. Journal of Banking & Finance, 93, 92-104.