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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 | ||
| تاریخ دریافت: 28 اسفند 1401، تاریخ بازنگری: 24 خرداد 1402، تاریخ پذیرش: 18 تیر 1402 | ||
| 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 | ||
| مراجع | ||
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