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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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[1] D. Ahmadian, Estimation of the stock price of the stock exchange using the behavioral financial model and comparison of the results using the stock price-profit ratio and Markov chain, Invest. Knowledge 7 (2018), no. 26, 149–168. [2] Z. Alamatian and M. Vafaei-Jahan, Prediction of stock price trends in the Iran stock exchange based on the combination of Bayesian networks and hidden Markov model, Financ. Engin. Secur. Manag. 8 (2017), no. 33, 283–298. [3] D. K. Bebarta, T. Eswari Sudha, and R. Bisoyi, An intelligent stock forecasting system using a unify model of CEFLANN, HMM, and GA for stock time series phenomena, Emerg. ICT Bridg. Future-Proc. 49th Ann. Convent. Computer Soc. India CSI, Springer International Publishing, 2015, pp. 485–496. [4] M. Biglari Kami and M. Amiri, Prediction of stock behavior using the Markov chain model, J. Financ. Engin. Secur. Manag. 20 (2014), 79–94. [5] M.W. Burkett, W.T. Scherer, and A. Todd, Portfolio design and management through state-based analytics: A probabilistic approach, Cogent Econ. Finance 8 (2020), no. 1, 1854948. [6] S.M.R. Davoudi and K. Mirsaeidi, Analysis of the Tehran Stock Exchange index within the framework of Markov chains, J. Financ. Manag. Perspect. 9 (2019), no. 25, 31-57. [7] A. Gupta and B. Dhingra, Stock market prediction using hidden Markov models, Students Conf. Engin. Syst., IEEE, 2015, pp. 1–4. [8] V. Kostadinova, I. Georgiev, V. Mihova, and V. Pavlov, An application of Markov chains in stock price prediction and risk portfolio optimization, AIP Conf. Proc., AIP Pub. LLC, 2321 (2021), no. 1, 030018. [9] W. Liu, L. Yang, and B. Yu, KDE distributionally robust portfolio optimization with higher moment coherent risk, Ann. Oper. Res. 307 (2021), no. 1, 363–397. [10] S.E. Mirmohammadi, Z. Madanchi, M. Panahian, and H. Jabbari, Portfolio selection with a combined approach of risk parity and factor analysis based on Markov switching, Decision-Mak. Res. Oper. 7 (2021), no. 1, 129–142. [11] P. Pasricha, D. Selvamuthu, G. D’Amico, and R. Manca, Portfolio optimization of credit risky bonds: a semi-Markov process approach, Financ Innov. 6 (2020), no. 1, 1–14. [12] A. Rahmani and M. Dehghani Ashkezari, Enhanced indexing using a discrete Markov chain model and mixed conditional value-at-risk, Int. J. Finance Manag. Account. 6 (2021), no. 22, 69–80. [13] R. Ruiz-Cruz, C. Sedano, and O. Flores, Genetic optimization of a trading algorithm based on pattern recognition, IEEE Latin Amer. Conf. Comput. Intell. (LA-CCI), IEEE, 2019, pp. 1-6. [14] H. Ryou, H.H. Bae, H.S. Lee, and K.J. Oh, Momentum investment strategy using a hidden Markov model, Sustainability 12 (2020), no. 17, 7031. [15] M. Zandieh, Stock price prediction using a combination of hidden Markov model and Markov chain, J. Financ. Manag. Perspect. 5 (2015), no. 12, 27–40. | ||
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