Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал:
http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4427| Назва: | Coverage of the Coronavirus Pandemic through Entropy Measures |
| Автори: | Соловйов, Володимир Миколайович Bielinskyi, Andrii O. Kharadzjan, Natalia A. Хараджян, Наталя Анатоліївна Бєлінський, Андрій Олександрович |
| Ключові слова: | coronavirus Bitcoin cryptocurrency crash critical event measures of complexity entropy indicator-precursor |
| Дата публікації: | 23-бер-2021 |
| Видавництво: | CEUR Workshop Proceedings |
| Бібліографічний опис: | Soloviev V. N. Coverage of the coronavirus pandemic through entropy measures / Vladimir N. Soloviev, Andrii O. Bielinskyi, Natalia A. Kharadzjan // CEUR Workhop Proceedings. - Vol. 2832. - Pp. 24-42. |
| Короткий огляд (реферат): | The rapidly evolving coronavirus pandemic brings a devastating effect on the entire world and its economy as awhole. Further instability related to COVID-19will negatively affect not only on companies and financial markets, but also on traders and investors that have been interested in saving their investment, minimizing risks, and making decisions such as how to manage their resources, how much to consume and save, when to buy or sell stocks, etc., and these decisions depend on the expectation of when to expect next critical change. Trying to help people in their subsequent decisions, we demonstrate the possibility of constructing indicators of critical and crash phenomena on the example of Bitcoin market crashes for further demonstration of their efficiency on the crash that is related to the coronavirus pandemic. For this purpose, the methods of the theory of complex systems have been used. Since the theory of complex systems has quite an extensive toolkit for exploring the nonlinear complex system, we take a look at the application of the concept of entropy in finance and use this concept to construct 6 effective entropy measures: Shannon entropy, Approximate entropy, Permutation entropy, and 3 Recurrence based entropies. We provide computational results that prove that these indicators could have been used to identify the beginning of the crash and predict the future course of events associated with the current pandemic. |
| Опис: | [1] J. Lonski, Coronavirus may be a black swan like no other, https://www.moodysanalytics.com/-/media/article/2020/ weekly-market-outlook-coronavirus-may-be-black-swan-like-no-other.pdf?source= news_body_link, 2020. [2] T. Fetzer, L. Hensel, J. Hermle, C. Roth, Coronavirus perceptions and economic anxiety, The Review of Economics and Statistics (2020) 1–36. doi:10.1162/rest_a_00946. [3] M. Feldkircher, F. Huber, M. Pfarrhofer, Measuring the effectiveness of us monetary policy during the covid-19 recession, 2020. arXiv:2007.15419. [4] R. Cerqueti, V. Ficcadenti, Anxiety for the pandemic and trust in financial markets, 2020. arXiv:2008.01649. [5] P. F. Procacci, C. E. Phelan, T. Aste, Market structure dynamics during covid-19 outbreak, 2020. arXiv:2003.10922. [6] A. A. Toda, Susceptible-infected-recovered (sir) dynamics of covid-19 and economic impact, 2020. arXiv:2003.11221. [7] R. Anderson, J. Heesterbeek, D. Klinkenberg, T. Hollingsworth, Comment how will country-based mitigation measures influence the course of the covid-19 epidemic?, The Lancet 395 (2020) 921–1010. doi:10.1016/S0140-6736(20)30567-5. [8] B. M. Pavlyshenko, Regression approach for modeling covid-19 spread and its impact on stock market, 2020. arXiv:2004.01489. [9] M. Costola, M. Iacopini, C. R. M. A. Santagiustina, Public concern and the financial markets during the covid-19 outbreak, 2020. arXiv:2005.06796. [10] K. Arias-Calluari, F. Alonso-Marroquin, M. Nattagh-Najafi, M. Harré, Methods for forecasting the effect of exogenous risk on stock markets, 2020. arXiv:2005.03969. [11] M. Garcin, J. Klein, S. Laaribi, Estimation of time-varying kernel densities and chronology of the impact of COVID-19 on financial markets, Working Papers hal-02901988, HAL, 2020. URL: https://ideas.repec.org/p/hal/wpaper/hal-02901988.html. [12] A. Ammy-Driss, M. Garcin, Efficiency of the financial markets during the COVID-19 crisis: time-varying parameters of fractional stable dynamics, Working Papers hal-02903655, HAL, 2020. URL: https://ideas.repec.org/p/hal/wpaper/hal-02903655.html. [13] A. F. Colladon, S. Grassi, F. Ravazzolo, F. Violante, Forecasting financial markets with semantic network analysis in the covid-19 crisis, 2020. arXiv:2009.04975. [14] N. Courtois, M. Grajek, R. Naik, Optimizing sha256 in bitcoin mining, Communications in Computer and Information Science 448 (2014) 131–144. doi:10.1007/ 978-3-662-44893-9_12. [15] L. Kristoufek, Grandpa, grandpa, tell me the one about bitcoin being a safe haven: New evidence from the covid-19 pandemic, Frontiers in Physics 8 (2020) 296. URL: https:// www.frontiersin.org/article/10.3389/fphy.2020.00296. doi:10.3389/fphy.2020.00296. [16] D. Broomhead, G. P. King, Extracting qualitative dynamics from experimental data, Physica D: Nonlinear Phenomena 20 (1986) 217 – 236. URL: http:// www.sciencedirect.com/science/article/pii/016727898690031X. doi:https://doi.org/ 10.1016/0167-2789(86)90031-X. [17] M. Rajkovic, Extracting meaningful information from financial data, Physica A: Statistical Mechanics and its Applications 287 (2000) 383–395. doi:10.1016/S0378-4371(00) 00377-0. [18] V. Ponomarenko, M. Prokhorov, Extracting information masked by the chaotic signal of a time-delay system, Physical review. E, Statistical, nonlinear, and soft matter physics 66 (2002) 026215. doi:10.1103/PhysRevE.66.026215. [19] M. Henry, G. Judge, Permutation Entropy and Information Recovery in Nonlinear Dynamic Economic Time Series, Econometrics 7 (2019) 1–16. URL: https://ideas.repec.org/ a/gam/jecnmx/v7y2019i1p10-d213039.html. [20] H. Sigaki, M. Perc, H. Valentin Ribeiro, Clustering patterns in efficiency and the comingof-age of the cryptocurrency market, Scientific Reports 9 (2019) 1440. doi:10.1038/ s41598-018-37773-3. [21] S. Pincus, R. E. Kalman, Irregularity, volatility, risk, and financial market time series, Proceedings of the National Academy of Sciences 101 (2004) 13709–13714. URL: https: //www.pnas.org/content/101/38/13709. doi:10.1073/pnas.0405168101. [22] A. Delgado-Bonal, Quantifying the randomness of the stock markets, Scientific Reports 9 (2019). doi:10.1038/s41598-019-49320-9. [23] S. Çalik, K. A. Metin, Entropy approach for volatility of wind energy, Thermal Science 23 (2019) 1863–1874. [24] D. T. Pele, M. Mazurencu, Using high-frequency entropy to forecast bitcoin’s daily value at risk, Entropy 21 (2019) 102. doi:10.3390/e21020102. [25] A. Belinskyi, V. Soloviev, S. Semerikov, V. Solovieva, Detecting stock crashes using levy distribution, in: A. Kiv, S. Semerikov, V. Soloviev, L. Kibalnyk, H. Danylchuk, A. Matviychuk (Eds.), Proceedings of the 8th. International Conference on Monitoring, Modeling & Managment of Emergent Economy, volume 2422 of POPL ’79, CEUR Workshop Proceedings, Odessa, Ukraine, 2019, pp. 226–236. doi:10.1145/567752.567774. [26] A. Bielinskyi, S. Semerikov, V. Solovieva, V. Soloviev, Levy´s stable distribution for stock crash detecting, SHS Web Conf. 65 (2019) 06006. URL: https://doi.org/10.1051/shsconf/ 20196506006. doi:10.1051/shsconf/20196506006. [27] V. Derbentsev, S. Semerikov, O. Serdyuk, V. Solovieva, V. Soloviev, Recurrence based entropies for sustainability indices, E3S Web Conf. 166 (2020) 13031. URL: https://doi.org/ 10.1051/e3sconf/202016613031. doi:10.1051/e3sconf/202016613031. [28] V. Soloviev, A. Belinskiy, Complex Systems Theory and Crashes of Cryptocurrency Market: 14th International Conference, ICTERI 2018, Kyiv, Ukraine, May 14-17, 2018, Revised Selected Papers, 2019, pp. 276–297. doi:10.1007/978-3-030-13929-2_14. [29] V. Soloviev, A. Belinskij, Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors, in: V. Ermolayev, M. C. Suárez-Figueroa, V. Yakovyna, V. Kharchenko, V. Kobets, H. Kravtsov, V. Peschanenko, Y. Prytula, M. Nikitchenko, A. Spivakovsky (Eds.), Proceedings of the 13th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, volume 2104, CEUR Workshop Proceedings, Kyiv, Ukraine, 2018, pp. 116–127. [30] V. Soloviev, O. Serdiuk, Quantum econophysical precursors of cryptocurrency crashes, Cherkasy University Bulletin: Applied Mathematics. Informatics (2020) 3–16. doi:10. 31651/2076-5886-2019-1-3-16. [31] V. Soloviev, A. Bielinskyi, V. Solovieva, Entropy analysis of crisis phenomena for djia index, in: V. Ermolayev, F. Mallet, V. Yakovyna, V. Kharchenko, V. Kobets, A. Korniłowicz, H. Kravtsov, M. Nikitchenko, S. Semerikov, A. Spivakovsky (Eds.), Proceedings of the 15th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer, volume 2393, CEUR Workshop Proceedings, Kherson, Ukraine, 2019, pp. 434–449. [32] V. Soloviev, O. Serdiuk, S. Semerikov, O. Kohut-Ferens, Recurrence entropy and financial crashes, in: Proceedings of the 2019 7th International Conference on Modeling, Development and Strategic Management of Economic System (MDSMES 2019), Atlantis Press, 2019/10, pp. 385–388. URL: https://doi.org/10.2991/mdsmes-19.2019.73. doi:https: //doi.org/10.2991/mdsmes-19.2019.73. [33] V. Soloviev, S. Semerikov, V. Solovieva, Lempel-ziv complexity and crises of cryptocurrency market, in: Proceedings of the III International Scientific Congress Society of Ambient Intelligence 2020 (ISC-SAI 2020), Atlantis Press, 2020, pp. 299–306. URL: https://doi. org/10.2991/aebmr.k.200318.037. doi:https://doi.org/10.2991/aebmr.k.200318. 037. [34] V. Soloviev, A. Bielinskyi, O. Serdyuk, V. Solovieva, S. Semerikov, Lyapunov exponents as indicators of the stock market crashes, in: O. Sokolov, G. Zholtkevych, V. Yakovyna, Y. Tarasich, V. Kharchenko, V. Kobets, O. Burov, S. Semerikov, H. Kravtsov (Eds.), Proceedings of the 16th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kharkiv, Ukraine, October 06-10, 2020, volume 2732 of CEUR Workshop Proceedings, CEUR-WS.org, 2020, pp. 455–470. URL: http://ceur-ws.org/Vol-2732/20200455.pdf. [35] V. Soloviev, V. Solovieva, A. Tuliakova, Visibility graphs and precursors of stock crashes, Neuro-Fuzzy Modeling Techniques in Economics (2019) 3–29. doi:10.33111/nfmte. 2019.003. [36] A. Bielinskyi, S. Semerikov, O. Serdyuk, V. Solovieva, V. Soloviev, L. Pichl, Econophysics of sustainability indices, CEUR Workshop Proceedings 2713 (2020) 372–392. [37] R. Clausius, The Mechanical Theory of Heat: With its Applications to the Steam-Engine and to the Physical Properties of Bodies, J. Van Voorst, 1867. [38] J. W. Gibbs, Elementary Principles in Statistical Mechanics: Developed with Especial Reference to the Rational Foundation of Thermodynamics, Cambridge Library Collection - Mathematics, Cambridge University Press, 2010. doi:10.1017/CBO9780511686948. [39] L. Boltzmann, Weitere studien über das wärmegleichgewicht unter gasmolekülen, Wiener Berichte 66 (1872) 275–370. [40] C. Chakrabarti, I. Chakrabarty, Boltmann-shannon entropy: Generalization and application, Modern Physics Letters B 20 (2006) 1471–1479. URL: https://doi.org/10.1142/ S0217984906011529. doi:10.1142/S0217984906011529. [41] C. E. Shannon, A mathematical theory of communication, The Bell System Technical Journal 27 (1948) 379–423. doi:10.1002/j.1538-7305.1948.tb01338.x. [42] T. Constantino, Introduction to Nonextensive Statistical Mechanics: Approaching a Complex World, 1st ed., Springer-Verlag New York, 2009. [43] R. Sole, S. Valverde, Information Theory of Complex Networks: On Evolution and Architectural Constraints, volume 207, 2004, pp. 189–207. doi:10.1007/ 978-3-540-44485-5_9. [44] J. S. Richman, J. R. Moorman, Physiological time-series analysis using approximate entropy and sample entropy, American Journal of Physiology-Heart and Circulatory Physiology 278 (2000) H2039–H2049. URL: https://doi.org/10. 1152/ajpheart.2000.278.6.H2039. doi:10.1152/ajpheart.2000.278.6.H2039. arXiv:https://doi.org/10.1152/ajpheart.2000.278.6.H2039, pMID: 10843903. [45] S. M. Pincus, Approximate entropy as a measure of system complexity, Proceedings of the National Academy of Sciences 88 (1991) 2297–2301. URL: https://www.pnas.org/content/88/6/2297. doi:10.1073/pnas.88.6.2297. arXiv:https://www.pnas.org/content/88/6/2297.full.pdf. [46] C. Bandt, B. Pompe, Permutation entropy: a natural complexity measure for time series., Physical review letters 88 17 (2002) 174102. [47] J. Amigó, Permutation Complexity in Dynamical Systems: Ordinal Patterns, Permutation Entropy and All That (Springer Series in Synergetics), 2010th ed., Springer, Reading, MA., 2010. [48] M. Zanin, L. Zunino, O. Rosso, D. Papo, Permutation entropy and its main biomedical and econophysics applications: A review, Entropy 14 (2012) 1553. doi:10.3390/e14081553. [49] H. Kantz, T. Schreiber, Nonlinear Time Series Analysis, 2 ed., Cambridge University Press, 2003. doi:10.1017/CBO9780511755798. [50] R. Gu, Multiscale shannon entropy and its application in the stock market, Physica A: Statistical Mechanics and its Applications 484 (2017) 215 – 224. URL: http://www.sciencedirect.com/science/article/pii/S0378437117304740. doi:https: //doi.org/10.1016/j.physa.2017.04.164. [51] F. Takens, Detecting strange attractors in turbulence, volume 898, 1981, p. 366. doi:10. 1007/BFb0091924. [52] C. L. Webber, J. P. Zbilut, Dynamical assessment of physiological systems and states using recurrence plot strategies, Journal of Applied Physiology 76 (1994) 965–973. URL: https://doi.org/10.1152/jappl.1994.76.2.965. doi:10.1152/jappl.1994.76.2.965. arXiv:https://doi.org/10.1152/jappl.1994.76.2.965, pMID: 8175612. [53] J. P. Zbilut, C. L. Webber, Embeddings and delays as derived from quantification of recurrence plots, Physics Letters A 171 (1992) 199 – 203. URL: http:// www.sciencedirect.com/science/article/pii/037596019290426M. doi:https://doi.org/ 10.1016/0375-9601(92)90426-M. [54] N. Marwan, N. Wessel, U. Meyerfeld, A. Schirdewan, J. Kurths, Recurrence-plot-based measures of complexity and their application to heart-rate-variability data, Physical Review E 66 (2002) 026702. doi:10.1103/PhysRevE.66.026702. [55] H. Rabarimanantsoa, L. Achour, C. Letellier, A. Cuvelier, J.-F. Muir, Recurrence plots and shannon entropy for a dynamical analysis of asynchronisms in noninvasive mechanical ventilation, Chaos (Woodbury, N.Y.) 17 (2007) 013115. doi:10.1063/1.2435307. [56] M. Little, P. Mcsharry, S. Roberts, D. Costello, I. Moroz, Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection, Biomedical engineering online 6 (2007) 23. doi:10.1186/1475-925X-6-23. [57] G. Corso, T. L. Prado, G. Z. dos S. Lima, S. R. Lopes, A novel entropy recurrence quantification analysis, 2017. arXiv:1707.00944. [58] S. Lopes, T. Prado, G. Corso, G. dos S. Lima, J. Kurths, Parameter-free quantification of stochastic and chaotic signals, Chaos, Solitons & Fractals 133 (2020) 109616. URL: http://www.sciencedirect.com/science/article/pii/S0960077920300151. doi:https: //doi.org/10.1016/j.chaos.2020.109616. [59] H. Jang, J. Lee, An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information, IEEE Access 6 (2018) 5427– 5437. [60] L. Alessandretti, A. ElBahrawy, L. M. Aiello, A. Baronchelli, Anticipating cryptocurrency prices using machine learning, Complexity 2018 (2018) 1–16. URL: http://dx.doi.org/10. 1155/2018/8983590. doi:10.1155/2018/8983590. [61] L. Zheng, H. He, Share price prediction of aerospace relevant companies with recurrent neural networks based on pca, 2020. arXiv:2008.11788. [62] N. Gandal, H. Hałaburda, Can we predict the winner in a market with network effects? competition in cryptocurrency market, SSRN Electronic Journal (2016). doi:10.2139/ ssrn.2832836. [63] T. Guo, A. Bifet, N. Antulov-Fantulin, Bitcoin volatility forecasting with a glimpse into buy and sell orders, 2018 IEEE International Conference on Data Mining (ICDM) (2018). URL: http://dx.doi.org/10.1109/ICDM.2018.00123. doi:10.1109/icdm.2018.00123. [64] T. Guo, N. Antulov-Fantulin, Predicting short-term bitcoin price fluctuations from buy and sell orders, ArXiv abs/1802.04065 (2018). [65] A. Bielinskyi, I. Khvostina, A. Mamanazarov, A. Matviychuk, S. Semerikov, O. Serdyuk, V. Solovieva, V. Soloviev, Predictors of oil shocks. Econophysical approach in environmental science, IOP Conference Series: Earth and Environmental Science 628 (2021). doi:10.1088/1755-1315/628/1/012019, 8th International Scientific Conference on Sustainability in Energy and Environmental Science, ISCSEES 2020 ; Conference Date: 21 October 2020 Through 22 October 2020. |
| URI (Уніфікований ідентифікатор ресурсу): | http://ceur-ws.org/Vol-2832/paper02.pdf http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4427 https://doi.org/10.31812/123456789/4427 |
| ISSN: | 1613-0073 |
| Розташовується у зібраннях: | Кафедра інформатики та прикладної математики |
Файли цього матеріалу:
| Файл | Опис | Розмір | Формат | |
|---|---|---|---|---|
| paper02.pdf | article | 1.38 MB | Adobe PDF | Переглянути/Відкрити |
Усі матеріали в архіві електронних ресурсів захищені авторським правом, всі права збережені.