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Irreversibility of financial time series: a case of crisis

Bielinskyi, Andrii O.; Hushko, Serhii V.; Matviychuk, Andriy V.; Serdyuk, Oleksandr A.; Семеріков, Сергій Олексійович; Соловйов, Володимир Миколайович; Білінський, Андрій Іванович; Матвійчук, Андрій Вікторович; Сердюк, О. А.

URI: https://ceur-ws.org/Vol-3048/paper04.pdf
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/6975
https://doi.org/10.31812/123456789/6975

Date: 2021-12-18

Abstract:

The focus of this study to measure the varying irreversibility of stock markets. A fundamental idea of this study is that financial systems are complex and nonlinear systems that are presented to be non-Gaussian fractal and chaotic. Their complexity and different aspects of nonlinear properties, such as time irreversibility, vary over time and for a long-range of scales. Therefore, our work presents approaches to measure the complexity and irreversibility of the time series. To the presented methods we include Guzik’s index, Porta’s index, Costa’s index, based on complex networks measures, Multiscale time irreversibility index and based on permutation patterns measures. Our study presents that the corresponding measures can be used as indicators or indicator-precursors of crisis states in stock markets.

Description:

[1] I. Prigogine, From Being to Becoming: Time and Complexity in the Physical Sciences, W. H. Freeman, 1980. [2] M. Costa, A. L. Goldberger, C.-K. Peng, Multiscale entropy analysis of biological signals, Phys. Rev. E 71 (2005) 021906. URL: https://link.aps.org/doi/10.1103/PhysRevE.71.021906. doi: 10.1103/PhysRevE.71.021906 . [3] M. Costa, C.-K. Peng, A. Goldberger, Multiscale analysis of heart rate dynamics: Entropy and time irreversibility measures, Cardiovascular Engineering 8 (2008) 88–93. [4] J. Donges, R. Donner, J. Kurths, Testing time series irreversibility using complex network methods, EPL 102 (2013) 10004. [5] M. Zanin, A. Rodríguez-González, E. Menasalvas Ruiz, D. Papo, Assessing time series reversibility through permutation patterns, Entropy 20 (2018). doi: 10.3390/e20090665 . [6] R. Flanagan, L. Lacasa, Irreversibility of financial time series: A graph-theoretical approach, Phys. Lett. A 380 (2016) 1689–1697. doi: 10.1016/j.physleta.2016.03.011 . [7] A. Puglisi, D. Villamaina, Irreversible effects of memory, EPL 88 (2009) 30004. doi: 10.1209/0295-5075/88/30004 . [8] A. Bielinskyi, S. Hushko, A. Matviychuk, O. Serdyuk, S. Semerikov, V. Soloviev, The lack of reversibility during financial crisis and its identification, SHS Web of Conferences 107 (2021) 03002. doi: 10.1051/shsconf/202110703002 . [9] C. S. Daw, C. E. A. Finney, M. B. Kennel, Symbolic approach for measuring temporal “irreversibility”, Phys. Rev. E 62 (2000) 1912–1921. URL: https://link.aps.org/doi/10.1103/PhysRevE.62.1912. doi: 10.1103/PhysRevE.62.1912 . [10] C. Diks, J. van Houwelingen, F. Takens, J. DeGoede, Reversibility as a criterion for discriminating time series, Phys. Lett. A 201 (1995) 221 – 228. doi: 10.1016/0375-9601(95)00239-Y . [11] P. Guzik, J. Piskorski, T. Krauze, A. Wykretowicz, H. Wysocki, Heart rate asymmetry by Poincaré plots of RR intervals, Biomedizinische Technik. Biomedical engineering 51 (2006) 272–275. doi: 10.1515/BMT.2006.054 . [12] M. B. Kennel, Testing time symmetry in time series using data compression dictionaries, Phys. Rev. E 69 (2004) 056208. URL: https://link.aps.org/doi/10.1103/PhysRevE.69.056208.doi: 10.1103/PhysRevE.69.056208 . [13] L. Lacasa, A. Nuñez, E. Roldán, J. Parrondo, B. Luque, Time series irreversibility: a visibility graph approach, Eur. Phys. J. B 85 (2012) 217. doi: 10.1140/epjb/e2012-20809-8 . [14] A. Porta, S. Guzzetti, N. Montano, T. Gnecchi-Ruscone, R. Furlan, A. Malliani, Time reversibility in short-term heart period variability, in: 2006 Computers in Cardiology, volume 2006, IEEE, 2006, pp. 77–80. [15] V. Soloviev, V. Solovieva, A. Tuliakova, Visibility graphs and precursors of stock crashes, Neuro-Fuzzy Modeling Techniques in Economics 8 (2019) 3–29. doi: 10.33111/nfmte.2019.003 . [16] V. Soloviev, V. Solovieva, A. Tuliakova, A. Hostryk, L. Pichl, Complex networks theory and precursors of financial crashes, CEUR Workshop Proceedings 2713 (2020) 53–67. [17] V. Soloviev, A. Bielinskyi, O. Serdyuk, V. Solovieva, S. Semerikov, Lyapunov exponents as indicators of the stock market crashes, CEUR Workshop Proceedings 2732 (2020) 455–470. [18] A. Bielinskyi, S. Semerikov, V. Solovieva, V. Soloviev, Levy’s stable distribution for stock crash detecting, SHS Web of Conferences 65 (2019) 06006. doi: 10.1051/shsconf/20196506006 . [19] V. Soloviev, A. Bielinskyi, V. Solovieva, Entropy analysis of crisis phenomena for DJIA index, CEUR Workshop Proceedings 2393 (2019) 434–449. [20] V. N. Soloviev, A. Belinskiy, Complex systems theory and crashes of cryptocurrency market, Communications in Computer and Information Science 1007 (2019) 276–297. doi: 10.1007/978-3-030-13929-2_14 . [21] V. N. Soloviev, S. P. Yevtushenko, V. V. Batareyev, Comparative analysis of the cryptocurrency and the stock markets using the Random Matrix Theory, CEUR Workshop Proceedings 2546 (2019) 87–100. [22] V. Soloviev, A. Belinskij, Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors, CEUR Workshop Proceedings 2104 (2018) 116–127. [23] A. O. Bielinskyi, I. Khvostina, A. Mamanazarov, A. Matviychuk, S. Semerikov, O. Serdyuk, V. Solovieva, V. N. Soloviev, Predictors of oil shocks. Econophysical approach in environmental science, IOP Conference Series: Earth and Environmental Science 628 (2021) 012019. doi: 10.1088/1755-1315/628/1/012019 . [24] V. N. Soloviev, A. O. Bielinskyi, N. A. Kharadzjan, Coverage of the coronavirus pandemic through entropy measures, CEUR Workshop Proceedings 2832 (2020) 24–42. [25] M. Costa, A. L. Goldberger, C.-K. Peng, Broken asymmetry of the human heartbeat: Loss of time irreversibility in aging and disease, Phys. Rev. Lett. 95 (2005) 198102. URL: https://link.aps.org/doi/10.1103/PhysRevLett.95.198102. doi: 10.1103/PhysRevLett.95.198102 . [26] L. Lacasa, B. Luque, F. Ballesteros, J. Luque, J. C. Nuño, From time series to complex networks: The visibility graph, Proceedings of the National Academy of Sciences 105 (2008) 4972. doi: 10.1073/pnas.0709247105 . [27] B. Luque, L. Lacasa, F. Ballesteros, J. Luque, Horizontal visibility graphs: Exact results for random time series, Physical Rev. E 80 (2009) 046103. doi: 10.1103/PhysRevE.80.046103 . [28] M. E. J. Newman, The structure and function of complex networks, SIAM Rev. 45 (2003) 167–256. doi: 10.1137/s003614450342480 . [29] D. Jou, J. Casas-Vázquez, G. Lebon, Extended irreversible thermodynamics, Reports on Progress in Physics 51 (1999) 1105. doi: 10.1088/0034- 4885/51/8/002 . [30] C. Bandt, B. Pompe, Permutation entropy: A natural complexity measure for time series, Phys. Rev. Lett. 88 (2002) 174102. URL: https://link.aps.org/doi/10.1103/PhysRevLett.88.174102. doi: 10.1103/PhysRevLett.88.174102 . [31] M. Zanin, L. Zunino, O. A. Rosso, D. Papo, Permutation entropy and its main biomedical and econophysics applications: A review, Entropy 14 (2012) 1553–1577. URL: https://www.mdpi.com/1099-4300/14/8/1553. doi: 10.3390/e14081553 .

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