Hidden Markov models and neural networks in formation of investment portfolio
P.A. Novikov, R.R. Valiev
Kazan Federal University, Kazan, 420008 Russia
Abstract
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, perceptron, investment portfolio, gross domestic product, Baum–Welch algorithm
References
1. Shklyaev A.O. Predicting financial time series by hidden Markov models. Nauchn. Zap. Molodykh Issled., 2015, vol. 1, pp. 17–21. (In Russian)
2. Waqasa Y., Hashmia S.H., Nazir M.I. Macroeconomic factors and foreign portfolio investment volatility: A case of South Asian countries. Future Bus. J., 2015, vol. 1, no. 1, pp. 65–74. doi: 10.1016/j.fbj.2015.11.002.
3. Leng C., Wang S. Hidden Markov Model for predicting the turning points of GDP fluctuation. Proc. Int. Conf. on Future Computer and Communication Engineering. Tianjin, Atlantis Press, 2014. doi: 10.2991/icfcce-14.2014.1.
4. Anghelache C., Anghelache G.V. Macroeconomic models used in the structural analysis of the gross domestic product. Rom. Stat. Rev., 2013, vol. 61, no. 6, pp. 15–21.
5. Geras'kin M.I., Porubova P.V. Trend analysis of the dynamics of macroeconomic indicators of the Russian Federation in 1959-2014. Vestn. Samar. Gos. Ekon. Univ., 2017, vol. 4, no. 150, pp. 5–18. (In Russian)
6. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the em algorithm. J. R. Stat. Soc., Ser. B, 1977, vol. 39, no. 1, pp. 1–38.
7. Fuh Ch.-D. SPRT and CUSUM in hidden Markov models. Ann. Stat., 2003, vol. 31, no. 3, pp. 942–977.
8. Rumelhart D.E., Hinton G.E., Williams R.J. Learning representations by back-propagating errors. Nature, 1986, vol. 323, no. 6088, pp. 533–536.
9. Ioan C.A., Ioan G. Analysis of the evolution of the gross domestic product by means of cyclic regressions. Acta Univ. Danubius, 2011, vol. 4, no. 4, pp. 114–126.
10. Savin A.N., Timofeeva N.E., Geras'kin A.S., Shamshina E.A., Mavlyutova Yu.A. Development of components of a program complex for stream filtration of audiocontent by means of hidden Markov models. Izv. Sarat. Univ., Nov. Ser. Mat. Mekh. Inf., 2015, vol. 15, no. 3, pp. 340–350. (In Russian)
11. Visser I., Speekenbrink M. depmixs4: An r package for hidden Markov models. J. Stat. Software, 2010, vol. 36, no. 7, pp. 1–21.
12. Viterbi A.J. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory, 1967, vol. 13, no. 2, pp. 260–269.
13. Tobin J. Liquidity preference as behavior towards risk. Rev. Econ. Stud., 1958, vol. 25, no. 2, pp. 65–86. doi: 10.2307/2296205.
14. Yahoo Finance. GSPC Interactive Stock Chart. S&P 500 Stock. Available at: https://finance.yahoo.com.
Received
November 10, 2017
For citation: Novikov P.A., Valiev R.R. Hidden Markov models and neural networks in formation of investment portfolio. Uchenye Zapiski Kazanskogo Universiteta. Seriya Fiziko-Matematicheskie Nauki, 2018, vol. 160, no. 2, pp. 357–363.
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