Hidden Markov models and neural networks in formation of investment portfolio

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...

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Published inUčënye zapiski Kazanskogo universiteta. Seriâ Fiziko-matematičeskie nauki Vol. 160; no. 2; pp. 357 - 363
Main Authors P.A. Novikov, R.R. Valiev
Format Journal Article
LanguageEnglish
Published Kazan Federal University 01.01.2018
Subjects
baum–welch algorithm
gross domestic product
hidden markov models
investment portfolio
perceptron
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Summary: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.
ISSN:2541-7746
2500-2198