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http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4121
Назва: | Recurrence plot-based analysis of financial-economic crashes |
Автори: | Соловйов, Володимир Миколайович Serdiuk, Oleksandr Семеріков, Сергій Олексійович Ків, Арнольд Юхимович |
Ключові слова: | complex systems recurrence entropy indicator-predictor of crashes |
Дата публікації: | 26-жов-2020 |
Видавництво: | Arnold Kiv |
Бібліографічний опис: | Soloviev V. Recurrence plot-based analysis of financial-economic crashes [Electronic resource] / Vladimir Soloviev, Oleksandr Serdiuk, Serhiy Semerikov, Arnold Kiv // Machine Learning for Prediction of Emergent Economy Dynamics 2020 : Proceedings of the Selected Papers of the Special Edition of International Conference on Monitoring, Modeling & Management of Emergent Economy (M3E2-MLPEED 2020), Odessa, Ukraine, July 13-18, 2020 / Edited by : Arnold Kiv // CEUR Workshop Proceedings. – 2020. – Vol. 2713. – Pp. 21-40. – Access mode : http://ceur-ws.org/Vol-2713/paper01.pdf |
Короткий огляд (реферат): | The article considers the possibility of analyzing the dynamics of changes in the characteristics of time series obtained on the basis of recurrence plots. The possibility of using the studied indicators to determine the presence of critical phenomena in economic systems is considered. Based on the analysis of economic time series of different nature, the suitability of the studied characteristics for the identification of critical phenomena is assessed. The description of recurrence diagrams and characteristics of time series that can be obtained on their basis is given. An analysis of seven characteristics of time series, including the coefficient of self-similarity, the coefficient of predictability, entropy, laminarity, is carried out. For the entropy characteristic, several options for its calculation are considered, each of which allows the one to get its own information about the state of the economic system. The possibility of using the studied characteristics as precursors of critical phenomena in economic systems is analyzed. We have demonstrated that the entropy analysis of financial time series in phase space reveals the characteristic recurrent properties of complex systems. The recurrence entropy methodology has several advantages compared to the traditional recurrence entropy defined in the literature, namely, the correct evaluation of the chaoticity level of the signal, the weak dependence on parameters. The characteristics were studied on the basis of daily values of the Dow Jones index for the period from 1990 to 2019 and daily values of oil prices for the period from 1987 to 2019. The behavior of recurrence entropy during critical phenomena in the stock markets of the USA, Germany and France was studied separately. As a result of the study, it was determined that delay time measure, determinism and laminarity can be used as indicators of critical phenomena. It turned out that recurrence entropy, unlike other entropy indicators of complexity, is an indicator and an early precursor of crisis phenomena. The ways of further research are outlined. |
Опис: | 1. Arunachalam, S.P., Kapa, S., Mulpuru, S.K., Friedman, P.A., Tolkacheva, E.G.: Rotor pivot point identification using recurrence period density entropy. In: 54th Annual Rocky Mountain Bioengineering Symposium, RMBS 2017 and 54th International ISA Biomedical Sciences Instrumentation Symposium 2017, 2017-March 2. Bielinskyi, A., Soloviev, V., Semerikov, S., Solovieva, V.: Detecting stock crashes using Levy distribution. CEUR Workshop Proceedings 2422, 420–433 (2019) 3. Corso, G., Prado, T., Lima, G., Lopes, S.: A novel entropy recurrence quantification analysis. arXiv:1707.00944v1 [stat.OT] (2017) 4. Danylchuk, H., Derbentsev, V., Soloviev, V., Sharapov, A.: Entropy analysis of dynamics properties of regional stock market. Science and Education a New Dimension. Economics 4(2), 15–19 (2016_ 5. Derbentsev, V., Semerikov, S., Serdyuk, O., Solovieva, V., Soloviev, V.: Recurrence based entropies for sustainability indices. E3S Web of Conferences 166, 13031 (2020). doi:10.1051/e3sconf/202016613031 6. Diaz, J.F.T.: Evidence of Noisy Chaotic Dynamics in the Returns of Four Dow Jones Stock Indices. Annual Review of Chaos Theory, Bifurcation and Dynamical System 4, 1–15 (2013) 7. Faure P., Lesne, A.: Estimating Kolmogorov entropy from recurrence plots. In: Webber, G.C.L., Marwan, N. (eds.) Recurrence Quantification Analysis, pp. 45–63. Springer International Publishing, Cham (2015) 8. Gu, R.: Multiscale Shannon Entropy and its application in the stock market. Physica A: Statistical Mechanics and its Applications 484, 215–224 (2017) 9. Kantz, H., Shreiber, T.: Nonlinear time series analysis, 2nd edn. Cambridge University Press, Cambridge (2004) 10. Lim, J.R.: Rapid Evaluation of Permutation Entropy for Financial Volatility Analysis – A Novel Hash Function using Feature-Bias Divergence”. Department of Computer Science, Imperial College of London, London. https://www.doc.ic.ac.uk/teaching/distinguishedprojects/2014/j.lim.pdf (2014). Accessed 17 Aug 2020 11. Little, M.A., McSharry, P.E., Roberts, S.J., Costello, D.A.E., Moroz, I.M.: Exploiting nonlinear recurrence and fractal scaling properties for voice disorder detection. BioMedical Engineering OnLine 6, 23 (2007) 12. Lopes, S., Lima, T., Corso, G., Lima, G., Kurths, J.: Parameter-free quantification of stochastic and chaotic signals. arXiv: 1905.02284v1 [physics.data-au] (2019) 13. Marwan, N., Carmen Romano, M., Thiel, M., Kurths, J.: Recurrence plots for the analysis of complex systems. Phys. Rep. 438(5–6), 237–329 (2007) 14. Pele, T., Lazar, E., Dufour, A.: Information Entropy and Measures of Market Risk. Entropy 19(5), 1–19 (2017) 15. Rabarimanantsoa, H., Achour, L., Letellier, C., Cuvelier, A., Muir, J.-F.: Recurrence plots and Shannon entropy for a dynamical analysis of asynchronisms in noninvasive mechanical ventilation. Chaos 17, 013115 (2007) 16. Soloviev, V., Bielinskyi, A., Solovieva, V.: Entropy analysis of crisis phenomena for DJIA index. CEUR Workshop Proceedings 2393, 434–449 (2019) 17. Soloviev, V.N., Belinskiy, A.: Complex Systems Theory and Crashes of Cryptocurrency Market. Communications in Computer and Information Science 1007, 276–297 (2019) 18. Sornette, D.: Why Stock Markets Crash: Critical Events in Complex Systems. Princeton University Press, Princeton (2003) 19. Wang, G.-J., Xie, C., Han, F.: Multi-Scale Approximate Entropy Analysis of Foreign Exchange Markets Efficiency. Systems Engineering Procedia 3, 201–208 (2012) 20. Webber, C.L., Zbilut, J.P.: Recurrence quantification analysis of nonlinear dynamical systems. In: Riley, A., Van Orden. G.C. (eds.) Contemporary Nonlinear Methods for the Behavioral Sciences, pp. 26–94. http://www.nsf.gov/sbe/bcs/pac/nmbs/nmbs.jsp (2005). Accessed 21 Mar 2017 21. Zhou, R., Cai, R., Tong, G.: Applications of entropy in finance: A review. Entropy 15(11), 4909–4931 (2013) |
URI (Уніфікований ідентифікатор ресурсу): | http://ceur-ws.org/Vol-2713/paper01.pdf http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4121 https://doi.org/10.31812/123456789/4121 |
ISSN: | 1613-0073 |
Розташовується у зібраннях: | Кафедра інформатики та прикладної математики |
Файли цього матеріалу:
Файл | Опис | Розмір | Формат | |
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paper01.pdf | article | 2.67 MB | Adobe PDF | Переглянути/Відкрити |
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