The methodological approach to forecast modeling of financial markets price dynamics in view of the system complexity

Mikhail Kussy

Abstract


Purpose and subject of research

The aim of this paper is to identify the essential system characteristics of financial market and methodological development of algorithm, which is used for behavior of market price’s forecast modeling.

Research methodology

A theoretical framework is proposed investigation of the financial market’s essential characteristics as a complex socio-economic system. Subsequently, described system attributes, which can be measured quantitatively, it is proposed to use for the analysis and prediction of the price’s behavior in the financial market. It is proposed algorithm for researching of financial markets as complex systems that use the system complexity’s attributes.

Value results

The present study provides a starting-point for further research of complexity’s attributes in the financial markets.

Conclusions

The proposed algorithm not only makes the process of financial market research correctly, but can be used for other socio-economic systems after some adaptation.

Key words: forecast modeling, financial markets, price dynamics, system complexity.

Keywords


forecast modeling; financial markets; price dynamics; system complexity

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References


Agaev, A. and Kuperin, Yu. F., Multifractal analysis and local hoelder exponents approach to detecting stock markets crashes, e-print: http://arXiv:cond-mat/0407603.

Andersson, M. K., 1998. On the effects of imposing or ignoring long memory when forecasting. Working Paper Series in Economics and Finance, № 225, e-print: https://sfb649.wiwi.hu-berlin.de/fedc.../xaghtmlnode100.html.

Bouchaud, J. P., Economics needs a scientific revolution, e-print: http://www.nature.com/nature/journal/ v455/n7217/full/4551181a.html.

Avtonomov, V. S., 2006. Methodological problems of modern economics. Herald of the RAS, vol. 76, 3, pp. 203-208.

Pincus, S. and Kalman, R. E., 2004. Irregularity, volatility, risk, and financial market time series. Proc. Natl. Acad. Sci. USA., vol. 101, 38, pp. 13709-13714.

Polterovich, V. M./ Crisis of economics. Paper presented at a scientific seminar of the Economics and CEMI RAS "Unknown economy, e-print: http://www.cemi.rssi.ru.

Derbentsev, V. D., Serduk, A. A., Soloviev, V. N. and Sharapov, A. D. Synergistic and econophysic methods for research of dynamic and structural characteristics of economic systems. Monography (Cherkasy, Gate-Ukraine, 2010), 300 p.

Plerou, V., Gopikrishnan, P., Rosenow, B., Amaral, L. A. N., Guhr, T. and Stanley, H. E., 2000. Econophysics : financial time series from a statistical physics point of view. Physica A., 279, pp. 443-456.

Barkley, J. and Rosser, Jr., Dynamics of markets. Econophysics and finance, By Joseph L. McCauley, Cambridge University Press, 2004, 209 p.

Kondratenko, A., Physical models of economic systems. Classical and quantum economics, Novosibirsk, Nauka, 2005, 30 p.

Mantegna, R. N. and Stanley, H. E., An introduction to econophysics, Cambridge University Press, 2000, 144 p.

Zhang, V.-B. Synergetics economy. Time and changes and in the nonlinear theory economic theory, Moscow, Mir, 1999, 354 p.

Ljskutov, A. Yu. and Мichaylov, A. S. , Introduction to Synergy (Moscow: Nauka 1990), 248 с.

Mocherny, S., 2001. Synergistically approach to economic research, Economy of Ukraine, № 5. pp. 44-51.

Chaos. Theory in economics: methods, models and evidence / Edited by Dechert W. D., Edward Elgar PC, 1996, 36 p.

Gilmore, C. G., 1993. A new test for chaos. Journal of economic behavior and organization, 22, pp. 209-237.

Lorenz, Hanz-Valter, 1989. Nonlinear dynamical economics and chaotic motion, Springer-Verlag, 320 p.

Peters, E., 2004. Fractal analysis of financial markets: the application of chaos theory to investment and economics, M., Internet Trading, 304 p.

Peters, E., 2000. Chaos and order in the capital markets. Analytical view of cycles, prices, and market volatility, М., Mir, 333 p.

Ruelle, David, 2001. Randomness and chaos, Izhevsk: SIC "Regular and Chaotic Dynamics", 192 p.

Cohen, J. and Stewart, I., 1994. The collapse of chaos. Discovering simplicity in a complex world, London, Viking, 495 p.

From simplicity to complexity. Part II. Information – Interaction – Emergence / Ed. by K. Mainzer, A. Müller, W. G. Saltzer, Braunschweig; Wiesbaden, Vieweg, 1988, 2337 p.

Hal'chyns'kyi, A., 2007. Methodology of complex systems. Economy, 8, pp. 4-18.

Derbentsev, V. D., Soloviev, V. N. and Sharapov, A. D., 2006. Modern methods of complex financial and economic systems. Bulletin of the Ukrainian Academy of Banking, 1 (20), pp. 100-110.

Lega, Yu. G., Melnik, V. V. and Soloviev, V. N. 2012. The complexity of socio-economic systems. Proceedings of the Tauride Agrotechnological State University (Economics), 2 (18), vol. 6, pp. 85-99.

Zgurovsky, M. Z. and Pankratova, N. D., 2005. System analysis. Problems, methodology, application. K., Naukova dumka, 744 p.

Arshinov. V. I., 2011. Synergetics converges with the complexity. Problems of Philosophy, 4, pp. 73-83.

Shishova, O. B. and Davydova, I. I., Quantum logic of theory of diversification as displaying of synergy in complex economic systems, e-print http://archive.nbuv.gov.ua/portal/ soc_gum/nvnau_eamb/2012_169_1/12sob.pdf.

Kussy, M. Yu., 2011. Methodological fundamentals of reflexivity’s application in forecast modeling for market trend. Reflexivity processes in the economy: the concepts, models, apply aspects: monograph, ed. By R. N. Lepa: NAS UKRAINE, Inst. of industry economy. Donetsk, APEX, pp. 144-162.

Kiv, A., Soloviev, V. and Solovieva, K., 2013. Multiscaling of information complexity measures. Information technology and modelling in economics: towards interdisciplinarity: monograph, ed. Dr. Sci. V. Solovyev and others. Cherkasy, Brahma-Ukraine, publisher Vovchok, O. Y., pp. 12-23.

Batyr, A. V., Solovyev, V. N. and Scherba, V. V., 2013. Comparative analysis of recurrence and entropy measures the complexity. Information technology and modelling in economics: towards interdisciplinarity: monograph, ed. Dr. Sci. V. Solovyev and others. Cherkasy, Brahma-Ukraine, publisher Vovchok, O. Y., pp. 84-90.

Danilchuk, G. B., Lukyanchuk, O. S. and Solovyev, V. N., 2013. Using of multyscale permutation entropy for investigation of complexity. Information technology and modelling in economics: towards interdisciplinarity: monograph, ed. Dr. Sci. V. Solovyev and others. Cherkasy, Brahma-Ukraine, publisher Vovchok, O. Y., pp. 90-100.

Rybczynska, O. M., 2013. Irreversible degree of complexity. Information technology and modelling in economics: towards interdisciplinarity: monograph / ed. Dr. Sci. V. Solovyev and others. Cherkasy, Brahma-Ukraine, publisher Vovchok, O. Y., pp. 100-109.

Solovyev, V. N. and Stratiychuk, I. A., 2013. Features of the building and use of crisis indicators-precursors on the basis of scale-dependent Lyapunov parameter. Information technology and modelling in economics: towards interdisciplinarity: monograph / ed. Dr. Sci. V. Solovyev and others. Cherkasy, Brahma-Ukraine, publisher Vovchok, O. Y., pp. 109-115.

Solovyev, V. N. and Serduk, O. A., 2013. Use Tsallis entropy to measure the complexity of economic systems. Information technology and modelling in economics: towards interdisciplinarity: monograph / ed. Dr. Sci. V. Solovyev and others. Cherkasy, Brahma-Ukraine, publisher Vovchok, O. Y., pp. 115-130.


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