Volume 20, Issue 3 (9-2025)
J. Mon. Ec. 2025, 20(3): 347-361 |
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Afsharirad M, Mazaherifar P. Investigating the Effect of Futures Leverage on Constrained Cryptocurrency Portfolios Using Mean-Semi Variance Model and Invasive Weed Optimization Algorithm. J. Mon. Ec. 2025; 20 (3) :347-361
URL: http://jme.mbri.ac.ir/article-1-707-en.html
URL: http://jme.mbri.ac.ir/article-1-707-en.html
1- Kharazmi University
Abstract: (282 Views)
In financial markets, a primary concern for investors is achieving enhanced returns while effectively managing portfolio risk. Leveraged trading is one potential strategy for increasing returns; however, the fundamental question is to find the optimal degree of leverage that yields the most efficient investment portfolio. This paper investigates the optimal leverage level of cryptocurrency futures within a diversified portfolio comprising digital assets, employing a constrained mean semi-variance model and the Invasive Weed Optimization (IWO) algorithm. The dataset, sourced from Coin Market Cap, consists of daily returns for 10 cryptocurrencies and 5 futures contracts over the period from 2022 to 2025. The proposed model incorporates allocation constraints, wherein each asset in the portfolio is subject to upper and lower bounds on its weight. Due to the imposed constraints, the problem is not solvable via traditional quadratic programming techniques, necessitating the application of the IWO algorithm as the optimization method. Empirical results reveal that incorporating futures into a cryptocurrency portfolio does not inherently enhance its performance. While leverage may increase expected returns, it simultaneously elevates portfolio risk. Consequently, based on the Sortino ratio, the overall risk-adjusted performance of the portfolio does not necessarily improve with the use of leveraged futures.
Keywords: Portfolio Optimization, Cryptocurrency Market, Invasive Weed Optimization Algorithm, Mean Semi-Variance, Sortino Ratio
Type of Study: Original Research - Empirical |
Subject:
Economics
Received: 20 Jun 2025 | Accepted: 15 Sep 2025 | Published: 2 Nov 2025
Received: 20 Jun 2025 | Accepted: 15 Sep 2025 | Published: 2 Nov 2025
References
1. رفرنس های متنی مثل خروجی کراس رف را در اینجا وارد کرده و تایید کنید
Afsharirad, M., & Mazaherifar, P. (2018). Comparing Genetic Algorithm and Invasive Weed Optimization in portfolio optimization and comparing nonlinear AR model and simple mean in expected return forecasting. Financial Knowledge Quarterly of Securities Analysis, 38, 77-84. [In Persian]
2. Alidaee, B., & Wang, H., & Wang, W. (2025). Comparative study of portfolio optimization models for cryptocurrency and stock markets. Advanced Modeling and Optimization, 559-573. [DOI:10.1109/ACCESS.2025.3551628]
3. Bajars, J., & Reitalu, T. (2022). What is the effect of leveraged trading? Analysis of asymmetric volatility in the 20 largest cryptocurrencies from 2016 to 2022 (Working Paper No. 2022-4). Stockholm School of Economics in Riga. Retrieved
4. Baur, D. G., & Dimpfl, T. (2018). Asymmetric volatility in cryptocurrencies. Economics Letters, 173, 148-151. [DOI:10.1016/j.econlet.2018.10.008]
5. Beihaqi, H., Azizadeh, & Aghababaei, M. E. (2015). Investigating the impact of fuzzy returns on the efficiency of constrained optimal portfolios in Tehran Stock Exchange using the Bee Colony Algorithm (Master's thesis). [In Persian]
6. Bowala, S., & Singh, J. (2022). Optimizing portfolio risk of cryptocurrencies using data driven risk measures. Journal of Risk and Financial Management, 15(10), 427. [DOI:10.3390/jrfm15100427]
7. Busetti, F. R. (2000). Metaheuristic Approaches to Realistic Portfolio Optimisation [Master's thesis, University of South Africa]. University of South Africa Repository.
8. Chen, Z., Li, P., Wang, Z., & Zhang, B. (2024). Leveraged trading and stock returns: Evidence from international stock markets. International Review of Financial Analysis. Elsevier. [DOI:10.1016/j.finmar.2024.100907]
9. Eskandari, R., Panahian, H., Eskandari, R., & Ghodrati, H. (2024). Investigating the hedging potential of cryptocurrencies on investment risk in the coin and stock markets in Iran. Financial Research, 26(4),815-835. [DOI:10.22059/frj.2024.364914.1007505 [In Persian]]
10. Eslami Bidgoli, G., & Tayyebi Sani, E. (2014). Investment portfolio optimization based on value at risk using ant colony algorithm. Financial Engineering and Securities Management (Portfolio Management), 5 (18), 163-18 [In Persian]
11. Estrada, J. (2004). Mean-semivariance behaviour: an alternative behavioural model. Journal of Emerging Market Finance, 3(3), 231-248. [DOI:10.1177/097265270400300301]
12. Houssein, E. H., Abdel Gafar, M. H., Fawzy, N., & Sayed, A. Y. (2025). Recent metaheuristic algorithms for solving some civil engineering optimization problems. Scientific Reports, 15, 7929 [DOI:10.1038/s41598-025-90000-8]
13. Kamili, H., & Raffi, M. E. (2016). Portfolio optimization using the Bat algorithm. International Review on Computer and Software (IRECOS), 11(3), 277-283. [DOI:10.15866/irecos.v11i3.8154]
14. Kumar, A. S., Suvvari, A., & Hafsal, K. (2025). How safe are the safe haven assets? Insights from a novel cross frequency analysis. Energy Research Letters, 7(Early View). [DOI:10.46557/001c.138282]
15. Markowitz, H. M. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. [DOI:10.1111/j.1540-6261.1952.tb01525.x]
16. Mehrabian, A. R., & Lucas, C. (2006). A novel numerical optimization algorithm inspired from weed colonization. Ecological Informatics, 1(4), 355-366 [DOI:10.1016/j.ecoinf.2006.07.003]
17. Poon, H. K. (2025). Practical Portfolio Optimization with Metaheuristics: Pre-assignment Constraint and Margin Trading. arXiv preprint arXiv:2503.15965. [DOI:10.1007/978-3-032-14757-8_14]
18. Sortino, F. A., & Price, L. N. (1994). Performance measurement in a downside risk framework. The Journal of Investing, 3(3), 59-64. [DOI:10.3905/joi.3.3.59]
19. Vishweswaran, I., & Padmavathi, N. (2025). Cryptocurrency in 2025: Fad, future, or financial transformation. International Journal of Research Publication and Reviews, 6(4), 11978-11984.
20. Xu, Z. (2025). Dynamic Portfolio Optimization Using Reinforcement Learning in Cryptocurrency Markets. Academic Journal of Business & Management, 7(4), 223-231. [DOI:10.25236/AJBM.2025.070428]
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