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Financial time series prediction with the technology of complex Markov chains

Соловйов, Володимир Миколайович; Saptsin, V.; Chabanenko, D.

URI: http://elibrary.kdpu.edu.ua/handle/0564/1145
https://doi.org/10.31812/0564/1145

Date: 2010

Abstract:

In this research the technology of complex Markov chains, i.e. Markov chains with a memory is applied to forecast financial time-series. The main distinction of complex or high-order Markov chains [1] and simple first-order ones is the existing of after effect or memory. The high-order Markov chains can be simplified to first-order ones by generalizing the states in Markov chains. Considering the “generalized state” as the sequence of states makes a possibility to model high-order Markov chains like first-order ones. The adaptive method of defining the states is proposed, it is concerned with the statistic properties of price returns [2]. According to the fundamental principles of quantum measurement theories, the measurement procedure impacts not only on the result of the measurement, but also on the state of the measured system, and the behaviour of this system in the future remains undefined, despite of the precision of the measurement. This statement, in our opinion, is general and is true not only for physical systems, but to any complex systems [3].

Description:

1. Tikhonov, V. I., Mironov, V. A. Markov processes. Moscow: Soviet Radio, 1977. 488 p. 2. Saptsin, V. Experience of application genetically complex Markov chains for neural networks technology prediction, Visnyk Krivoriz`kogo ekonomichnogo institutu KNEU, Kriviy Rig, 2009, Vol. 2 (18), pp. 56–66. 3. Saptsin, V., Soloviev, V. Relativistic quantum econophysics – new paradigms in complex systems modeling, arXiv: physics/ 0907.1142 [physics.soc-ph], 7 Jul 2009. 4. Bookinham, M. Noises in electronic devices and systems. Moscow: Mir, 1986. 5. Wasserman, P. D. Neural computing: theory and practice. New York: Van Nostrand Reinhold, 1989. 6. Surovcev, I. S., Klyukin, V. I., Pivovarova, R. P. Neural networks. Voronezh: VGU, 1994. (In Russian). 7. Ezhov, A. A., Shumskiy, S. A., Neurocomputing and his applications in an economy and business. Moscow: MIFI, 1998. (In Russian). 8. Mandelbrot, B. The fractal geometry of nature. San Francisco: Freeman, 1982. 9. Rabiner, R. L. A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition, Proceedings of the IEEE, Vol. 77 (2), 1989, pp. 257–286. 10. Weigend, A. S., Gershenfeld, N. A. Time Series Prediction: Forecasting the Future and Understanding the Past. Addison-Wesley, 1993. 11. Zhang, Y. Prediction of Financial Time Series with Hidden Markov Models. In: School of Computer Science, Vol. Master of Applied Science. Simon Fraser University, 2004 12. Soloviev, V., Saptsin, V., Chabanenko, D. Prediction of financial time series with the technology of high-order Markov chains. Working Group on Physics of Socio-economic Systems (AGSOE). Dresden, 2009. – URL – http://www.dpg-verhandlungen.de/2009/dresden/agsoe.pdf Appendix.

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