Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/3716
Повний запис метаданих
Поле DCЗначенняМова
dc.contributor.authorСоловйов, Володимир Миколайович-
dc.contributor.authorСемеріков, Сергій Олексійович-
dc.contributor.authorСоловйова, Вікторія Володимирівна-
dc.identifier.citationSoloviev V. Lempel-Ziv Complexity and Crises of Cryptocurrency Market [Electronic resource] / Vladimir Soloviev, Serhiy Semerikov, Victoria Solovieva // Proceedings of the III International Scientific Congress Society of Ambient Intelligence 2020 (ISC-SAI 2020) / Editors : Serhii Hushko, Victoria Solovieva. – P. 385-388. – (Advances in Economics, Business and Management Research, volume 129). – DOI : 10.2991/aebmr.k.200318.037. – Access mode : https://download.atlantis-press.com/article/125937244.pdfuk_UA
dc.identifier.otherDOI : 10.2991/aebmr.k.200318.037-
dc.description[1] S. Zozor, O. Blanc, V. Jacquemet, N. Virag, J.-M. Vesin, E. Pruvot, L. Kappenberger, C. Henriquez, A numerical scheme for modeling wavefront propagation on a monolayer of arbitrary geometry, IEEE Transactions on Biomedical Engineering. 50(4) (2003) 412–420. DOI: https://doi.org/10.1109/TBME.2003.809505 [2] N. Kannathal, S. K. Puthusserypady, L. C. Min. Complex dynamics of epileptic EEG. In Proc. of the 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS'04), 1 (2004) 604–607, San Fransisco, CA, USA [3] W. B. Arthur. Inductive reasoning and bounded rationality. The American Economic Review, 4(2) (1994) 406–411 [4] N. B. Tuma. Social Dynamics Models and methods. Elsevier (1984) [5] R. J. Shiller, S. Fischer, B. M. Friedman. Stock prices and social dynamics. Brookings papers on economic activity, 15(2) (1984) 457–510. DOI: https://doi.org/10.2307/2534436 [6] M. Rajkovic. Extracting meaningful information from financial data. Physica A, 287(3–4) (2000) 383– 395. DOI: https://doi.org/10.1016/S0378-4371(00)00377-0 [7] V. I. Ponomarenko, M. D. Prokhorov. Extracting information masked by the chaotic signal of a timedelay system. Physical Review E, 66(2) (2002) 026215. DOI: https://doi.org/ 10.1103/PhysRevE.66.026215 [8] D. S. Broomhead, G. P. King. Extracting qualitative dynamics from experimental data. Physica D, 20(2-3) (1986) 217–236. DOI: https://doi.org/ 10.1016/0167-2789(86)90031-X [9] R. Quian Quiroga, J. Arnhold, K. Lehnertz, P. Grassberger. Kulback-Leibler and renormalized entropies: Applications to electroencephalograms of epilepsy patients. Physical Review E, 62(6) (2000) 8380–8386. DOI: https://doi.org/ 10.1103/PhysRevE.62.8380 [10] O. A. Rosso, S. Blanco, J. Yordanova, V. Kolev, A. Figliola, M. Schurmann, E. Basar. Wavelet entropy: a new tool for analysis of short duration brain electrical signals. Journal of neuroscience methods, 5(1) (2001) 65–75. DOI: https://doi.org/10.1016/s0165-0270(00)00356-3 [11] T. Schreiber. Measuring information transfer. Physical Review Letters, 85(2) (2000) 461–464. DOI: https://doi.org/ 10.1103/PhysRevLett.85.461 [12] A. Wolf, J. B. Swift, H. L. Swinney, J. A. Vastano. Determining Lyapunov exponents from a time series. Physica D, 16(3) (1985) 285–317. DOI: https://doi.org/ 10.1016/0167-2789(85)90011-9 [13] R. Nagarajan. Quantifying physiological data with Lempel-Ziv complexity-certain issues. IEEE Transactions on Biomedical Engineering, 49(11) (2002) 1371–1373. DOI: https://doi.org/10.1109/TBME.2002.804582 [14] M. Aboy, R. Hornero, D. Abasolo, D. Alvarez. Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis. IEEE Transactions on Biomedical Engineering, 53(11) (2006) 2282–2288. DOI: https://doi.org/10.1109/TBME.2006.883696 [15] S. Zozor, P. Ravier, O. Buttelli. On Lempel-Ziv complexity for multidimensional data analysis. Physica A, 345(1–2) (2005) 285–302. DOI: https://doi.org/10.1016/j.physa.2004.07.025 [16] C. Vignat, J.-F. Bercher. Analysis of signals in the Fisher-Shannon information plane. Physics Letters A, 312(1–2) (2003) 27–33. DOI: https://doi.org/10.1016/S0375-9601(03)00570-X [17] S. Zozor, P. Ravier, and O. Buttelli, On LempelZiv complexity for multidimensional data analysis, Physica A: Statistical Mechanics and its Applications, 345(1–2) (2005) 285–302. DOI: https://doi.org/10.1016/j.physa.2004.07.025 [18] J.-L. Blanc, L. Pezard, and A. Lesne, Delay independence of mutual-information rate of two symbolic sequences, Phys. Rev. E, 84(3) (2011) 036214. DOI: https://doi.org/10.1103/PhysRevE.84.036214 [19] E. Estevez-Rams, R. Lora Serrano, B. Aragon Fernandez, I. Brito Reyes, On the non-randomness of maximum Lempel Ziv complexity sequences of finite size, Chaos, 23(2) (2013) 023118. DOI: https://doi.org/10.1063/1.4808251 [20] S. Halvin, R. Cohen, Complex networks. Structure, robustness and function. Cambridge University Press, New York (2010) http://ebooks.cambridge.org/ebook.jsf?bid=CBO97805 11780356 [21] H. Cao, Y. Li, Unraveling chaotic attractors by complex networks and measurements of stock market complexity, Chaos, 24 (2014) 013134. DOI: http://dx.doi.org/10.1063/1.4868258 [22] V.N. Soloviev, A. Belinskiy, (2019) Complex systems theory and crashes of cryptocurrency market. In: Ermolayev V. (eds) Information and Communication Technologies in Education, Research, and Industrial Applications. ICTERI 2018. Communications in Computer and Information Science, vol. 1007. Springer, Cham, 2019, pp. 276–297. DOI: https://doi.org/10.1007/978-3-030-13929-2_14 [23] G. Ricardo, R. Matsushita, S. Da Silva. The relative efficiency of stockmarkets, Economics Bulletin. 7(6) (2008) 1–12. [24] R. Giglio, R. Matsushita, A. Figueiredo, I. Gleria, S. Da Silva, Algorithmic complexity theory and the relative efficiency of financial markets, Europhysics Letters. 84(4) (2008) 48005. DOI: https://doi.org/10.1209/0295-5075/84/48005 [25] G. Ricardo, S. Da Silva. Ranking the stocks listed on Bovespa according to their relative efficiency, Appl. Math. Sci. 3(43) (2009) 2133–2142. [26] S. Da Silva, C. Taufemback, R. Giglio, Algorithmic complexity theory detects decreases in the relative efficiency of stock markets in the aftermath of the 2008 financial crisis, Economics Bulletin. 31(2) (2011) 1631–1647. [27] S. Da Silva, Financial market efficiency should be gauged in relative rather than absolute terms, J.Stock Forex Trad. 4(1) (2015). DOI: http://dx.doi.org/10.4172/2168-9458.1000140 [28] A. Lempel and J. Ziv, On the complexity of finite sequences, IEEE Transactions on InformationTheory. 22(1) (1976) 75–81. DOI: https://doi.org/10.1109/TIT.1976.1055501 [29] O. Brandouy, D. Jean-Paul, L. Ma, H. Zenil, Algorithmic complexity of financial motions, Research in International Business and Finance, 30 (2014). DOI: https://doi.org/10.1016/j.ribaf.2012.08.001 [30] P. Fiedor, Multiscale Analysis of the Predictability of Stock Returns, Riscs, 3 (2015) 219–233, DOI: https://doi.org/ 10.3390/risks3020219 [31] J. Gao, Y, Hou, F. Fan, F. Liu, Complexity Changes in the US and China’s Stock Markets: Differences, Causes, and Wider Social Implications, Entropy, 22(75) (2020), DOI: https://doi.org/10.3390/e22010075 [32] H. Cao, Y. Li, Unraveling chaotic attractors by complex networks and measurements of stock market Complexity, Chaos, 24 (2014) 0113134. DOI; http://dx.doi.org/10.1063/1.4868258 [33] D. Stosic, D. Stosic, T. Ludermir, T. Stosic, Exploring disorder and complexity in the cryptocurrency space, Physica A: Statistical Mechanics and its Applications, 525 (2019 548–556. DOI: https://doi.org/10.1016/j.physa.2019.03.091 [34] V.Soloviev, A. Belinski, Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors. In: Ermolayev V. (eds.) Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. CEUR Workshop Proceedings, 2014 (2018) 116–127. http://ceur-ws.org/Vol-2104/paper_175.pdf [35] A.N. Kolmogorov, Three approaches to the quantitative definition of information, Int. J. Comp. Math., 2(1-4) (1968) 157–168. DOI: https://doi.org/10.1080/00207166808803030-
dc.description.abstractThe informational (Kolmogorov) measure of complexity in accordance with the Lempel-Ziv algorithm (LZC) is calculated for the logarithmic returns of daily Bitcoin/$ values. The calculations were carried out for a moving window with a variation in its size (50–250 days) in increments of one day in the framework of the implemented coarse graining procedure. It is shown that in both mono-and multi-scaling versions, LZC is sensitive to noticeable fluctuations in the Bitcoin price that occur as a result of critical events in the cryptocurrency market. In equilibrium, stable state, having a relatively low value, LZC rapidly increases immediately before the crisis, which proves the dominance of the chaotic component of the time series. The classification and periodization of crisis phenomena in the cryptocurrency market for the period 2010–2020 has been carried out. The results demonstrate the possibility of using the LZC measure as an indicator-precursor of crisis phenomena in the cryptocurrency market.uk_UA
dc.publisherAtlantis Pressuk_UA
dc.relation.ispartofseriesAdvances in Economics, Business and Management Research;129-
dc.subjectinformation theoryuk_UA
dc.subjecttime seriesuk_UA
dc.subjectcomplex systemsuk_UA
dc.subjectKolmogorov complexityuk_UA
dc.subjectLempel-Ziv complexityuk_UA
dc.titleLempel-Ziv Complexity and Crises of Cryptocurrency Marketuk_UA
Розташовується у зібраннях:Кафедра інформатики та прикладної математики

Файли цього матеріалу:
Файл Опис РозмірФормат 
125937244.pdfarticle1.54 MBAdobe PDFПереглянути/Відкрити

Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.