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Entropy analysis of dynamic properties of regional stock markets

Danylchuk, H.; Derbentsev, V.; Соловйов, Володимир Миколайович; Sharapov, A.

URI: http://elibrary.kdpu.edu.ua/handle/0564/1154
https://doi.org/10.31812/0564/1154

Date: 2016

Abstract:

This paper examines entropy analysis of regional stock markets. We propose and empirically demonstrate the effectiveness of using such entropy as sample entropy, wavelet and Tsallis entropy as a measure of uncertainty and instability of such complex systems as regional stock markets. Our results show that these entropy measures can be effectively used as crisis prediction indicators.

Description:

1. Sornette D. (2003) Why Stock Markets Crash: Critical Events in Complex Financial Systems. Princeton University Press. 2. Peters, E. E. (1994). Fractal Market Analysis: Applying Chaos Theory to Investment and Economics. John Wiley and Sons. 3. Mantegna R. N. , Stanley H. E. (2000). An Introduction to Econophysics. – Cambridge: Cambridge University Press. 4. Derbentsev V., Serdyuk O., Soloviev V., Sharapov A. Synergetics and Econophysics Research Methods of Dynamic and Structural Properties of Economic Systems. Cherkasy: Brama-Ukraine, 2010. – 300 p. 5. Chyumak O. Entropy and Fractals in Data Analysis. Moscow: Regular and Chaotic Dynamics, 2011. – 164 p. 6. Weidlich W. Socio-Dynamics: A Systematic Approach to Mathematical Modelling in the Social Science. Taylor and Francis, London, 2002. 7. Soloviev V., Danilchuk H., Serdyuk O.A. Research of the crises phenomena on time series basis with help of entropy characteristics // Information Technologies, Management and Society. Theses of the International Conference. Information Technologies and Management. 2008 April 10- 11, Information System Institute, Riga, Latvia. – P.41-42. 8. Soloviev V., Serdyuk O. Using of wavelet entropy for analysis of complex economic systems // Econophysics Colloquium and beyond, Alcona, Italy, September 27-29, 2007. 9. Joshua S., Richman J., Moorman R. Physiological timeseries analysis using approximate entropy and sample entropy // Am. J Physiol. Heart Circ. Physiol. 278: H2039- H2049, 2000. 10. Pincus S.M. (1991) Approximate entropy as a measure of system complexity. Proc. Natl. Acad. Sci. Vol.88.  P. 2297-2301. 11. Tsallis C. Nonextensive Statistics: Theoretical, Experimental and Computational Evidences and Connections / C. Tsallis // Brazilian Journal of Physics. – Vol. 29. – №. 1. – March, 1999. 12. Tsallis C. Introduction to Nonextnsive Statistical Mechanics, Approaching a Complex Word / C. Tsallis // Springer, New York, 2009. – 382 p. 13. Rosson O.A, Blanco S, Yordanova J, et al. Wavelet entropy: a new tool for analysis of short duration brain electrical signals[J]. J Neurosci Meth, vol. 105, P. 65-75, Jan. 2001. 14. Quiroga, R.Q; Rosso, O.A; Basar E.; Schürmann M. Wavelet entropy in event-related potentials: A new method shows ordering of EEG oscillations. Biol. Cybern. 2001, 84, 291–299. 15. http://finance.yahoo.com.

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