Geodesic
Hidden Markov models and neural networks in formation of investment portfolio
Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki, Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, Tome 160 (2018) no. 2, pp. 357-363

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Common mathematical models are used by investors for prediction of the future state of the financial market lose if the macroeconomic situation gets worse. In this regard, it is desired to build models that minimize losses in the formation of an investment portfolio under the conditions of economic fluctuations. Many models describing economic fluctuations consider annual changes, which allows to reduce the response time to the actual economical fluctuations. A model that predicts the future state of the economy based on more recent data enables the choice of the optimal strategy for investment portfolio formation. In this paper, we have proposed an economical model that allows to determine possible direction of changes in the economic situation on a quarterly basis, which is helpful in making timely decisions on the strategy for investment portfolio formation. Our model is based on hidden Markov models and the multilayer perceptron.
Keywords: hidden Markov models, investment portfolio, gross domestic product, Baum–Welch algorithm.
Mots-clés : perceptron 
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     author = {P. A. Novikov and R. R. Valiev},
     title = {Hidden {Markov} models and neural networks in formation of investment portfolio},
     journal = {U\v{c}\"enye zapiski Kazanskogo universiteta. Seri\^a Fiziko-matemati\v{c}eskie nauki},
     pages = {357--363},
     publisher = {mathdoc},
     volume = {160},
     number = {2},
     year = {2018},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/UZKU_2018_160_2_a16/}
}
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P. A. Novikov; R. R. Valiev. Hidden Markov models and neural networks in formation of investment portfolio. Učënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki, Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, Tome 160 (2018) no. 2, pp. 357-363. http://geodesic.mathdoc.fr/item/UZKU_2018_160_2_a16/