Nabiyan, Ali, Baktash, Forozan, Davoodi, Sayyed Mohammad Reza. (1403). Portfolio design and optimization within the framework of the Markov chain. هنرهای کاربردی, 15(4), 265-273. doi: 10.22075/ijnaa.2023.30951.4523
Ali Nabiyan; Forozan Baktash; Sayyed Mohammad Reza Davoodi. "Portfolio design and optimization within the framework of the Markov chain". هنرهای کاربردی, 15, 4, 1403, 265-273. doi: 10.22075/ijnaa.2023.30951.4523
Nabiyan, Ali, Baktash, Forozan, Davoodi, Sayyed Mohammad Reza. (1403). 'Portfolio design and optimization within the framework of the Markov chain', هنرهای کاربردی, 15(4), pp. 265-273. doi: 10.22075/ijnaa.2023.30951.4523
Nabiyan, Ali, Baktash, Forozan, Davoodi, Sayyed Mohammad Reza. Portfolio design and optimization within the framework of the Markov chain. هنرهای کاربردی, 1403; 15(4): 265-273. doi: 10.22075/ijnaa.2023.30951.4523

Portfolio design and optimization within the framework of the Markov chain

International Journal of Nonlinear Analysis and Applications
مقاله 24، دوره 15، شماره 4، تیر 2024، صفحه 265-273    اصل مقاله (460.54 K)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22075/ijnaa.2023.30951.4523
نویسندگان
Ali Nabiyan1؛ Forozan Baktash* 2؛ Sayyed Mohammad Reza Davoodi1
1Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran
2Department of Economics, Dehagاan Branch, Islamic Azad University, Dehaghan, Iran
چکیده
Return and risk are significant parameters in selecting an optimal portfolio, depending on the portfolio return distribution. In a stochastic process, the Markov property causes the future distribution of a random process to be measurable according to the state-transition matrix and the initial process state. According to the main idea of the present study in the optimal portfolio selection, portfolio weights are chosen in a way that the Markov property is established for the portfolio return series and the distribution of future portfolio returns is close to the distribution of investor's expected returns; hence, K-L divergence (Kullback–Leibler divergence) is utilized as a criterion of closeness. Using this idea, an optimal portfolio selection model was designed and implemented in the present study. This optimal portfolio was optimized using a Markov approach and according to historical data of 10 indices on the Tehran Stock Exchange from 2009 to 2022 in a six-member state. The optimal portfolio performance evaluation using the Sharpe ratio and value at risk criteria indicated that the research model had a higher performance than the mean-variance and weight parity models.
کلیدواژه‌ها
Markov property؛ K-L divergence (Kullback–Leibler divergence) criterion؛ Return distribution؛ Goodness of fit (GoF) test
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