Будь ласка, використовуйте цей ідентифікатор, щоб цитувати або посилатися на цей матеріал: http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/2881
Повний запис метаданих
Поле DCЗначенняМова
dc.contributor.authorБєлінський, Андрій Олександрович-
dc.contributor.authorСоловйов, Володимир Миколайович-
dc.date.accessioned2018-12-29T06:45:21Z-
dc.date.available2018-12-29T06:45:21Z-
dc.date.issued2018-12-27-
dc.identifier.citationBielinskyi A. O. Complex network precursors of crashes and critical events in the cryptocurrency market / Andrii O. Bielinskyi, Vladimir N. Soloviev // Computer Science & Software Engineering : Proceedings of the 1st Student Workshop (CS&SE@SW 2018), Kryvyi Rih, Ukraine, November 30, 2018 / Edited by : Arnold E. Kiv, Serhiy O. Semerikov, Vladimir N. Soloviev, Andrii M. Striuk. – P. 41-52. – (CEUR Workshop Proceedings (CEUR-WS.org), Vol. 2292). – Access mode : http://ceur-ws.org/Vol-2292/paper02.pdfuk
dc.identifier.issn1613-0073-
dc.identifier.urihttp://elibrary.kdpu.edu.ua/xmlui/handle/123456789/2881-
dc.identifier.urihttps://doi.org/10.31812/123456789/2881-
dc.description1. Halvin, S., Cohen, R.: Complex networks. Structure, robustness and function. Cambridge University Press, New York (2010) 2. Albert, R., Barabási, A.-L.: Statistical Mechanics of Complex Networks. Rev. Mod. Phys. 74, 47–97 (2002). doi:10.1103/RevModPhys.74.47 3. Newman, M., Barabási A.-L., Watts D.J.: The Structure and Dynamics of Networks. Princeton University Press, Princeton (2006) 4. Newman, M.E.J.: The Structure and Function of Complex Networks. SIAM Reviews. 45(2), 167–256 (2003). doi:10.1137/S003614450342480 5. Nikolis, G., Prigogine, I.: Exploring Complexity: An Introduction. St. Martin’s Press, New York (1989) 6. Andrews, B., Calder, M., Davis, R.A.: Maximum Likelihood Estimation for α-Stable Autoregressive Processes. The Annals of Statistics. 37(4), 1946–1982 (2009). doi:10.1214/08-AOS632 7. Shah, D., Zhang, K.: Bayesian regression and Bitcoin. In: 2014 52nd Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, 30 Sept.-3 Oct. 2014. doi:10.1109/ALLERTON.2014.7028484 8. Chen, T., Guestrin, C.: XGBoost: A Scalable Tree Boosting System. In: KDD '16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, August 13-17, 2016, pp. 785-794 (2016). doi:10.1145/2939672.2939785 9. Alessandretti, L., ElBahrawy, A., Aiello, L.M., Baronchelli, A.: Machine Learning the Cryptocurrency Market. https://ssrn.com/abstract=3183792 (2018). doi:10.2139/ssrn.3183792. Accessed 15 Sep 2018 10. Guo, T., Antulov-Fantulin, N.: An experimental study of Bitcoin fluctuation using machine learning methods. arXiv:1802.04065v2 [stat.ML]. https://arxiv.org/pdf/1802.04065.pdf (2018). Accessed 15 Sep 2018 11. Peng Y., Albuquerque, P.H.M., de Sá, J.M.C., Padula, A.J.A., Montenegro, M.R: The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Systems with Applications. 97, 177–192 (2018). doi:10.1016/j.eswa.2017.12.004 12. Donier, J., Bouchaud J.-P.: Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights. PLoS ONE 10(10): e0139356 (2015). doi:10.1371/journal.pone.0139356 13. Di Francesco Maesa, D., Marino, A., Ricci, L.: Data-driven analysis of Bitcoin properties: exploiting the users graph. International Journal of Data Science and Analytics. 6(1), 63–80 (2018). doi:10.1007/s41060-017-0074-x 14. Bovet, A., Campajola, C., Lazo, J.F., Mottes, F., Pozzana, I., Restocchi, V., Saggese, P., Vallarano, N., Squartini, T., Tessone, C.J.: Network-based indicators of Bitcoin bubbles. arXiv:1805.04460v1 [physics.soc-ph]. https://arxiv.org/pdf/1805.04460 (2018). Accessed 11 Sep 2018 15. Kondor, D., Csabai, I., Szüle, J., Pόsfai, M., Vattay, G.: Inferring the interplay of network structure and market effects in Bitcoin. New Journal of Physics. 16, 125003 (2014). doi:10.1088/1367-2630/16/12/125003 16. Wheatley, S., Sornette, D., Huber, T., Reppen, M., Gantner, R.N.: Are Bitcoin Bubbles Predictable? Combining a Generalized Metcalfe’s Law and the LPPLS Model. Swiss Finance Institute Research Paper No. 18-22. https://ssrn.com/abstract=3141050 (2018). doi:10.2139/ssrn.3141050. Accessed 15 Sep 2018 17. Gerlach, J-C., Demos, G., Sornette, D.: Dissection of Bitcoin’s Multiscale Bubble History from January 2012 to February 2018. arXiv:1804.06261v2 [econ.EM]. https://arxiv.org/pdf/1804.06261 (2018). Accessed 15 Sep 2018 18. Soloviev, V., Belinskij, A.: Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors. In: Ermolayev, V., Suárez-Figueroa, M.C., Yakovyna, V., Kharchenko, V., Kobets, V., Kravtsov, H., Peschanenko, V., Prytula, Y., Nikitchenko, M., Spivakovsky, A. (eds.) Proceedings of the 14th International Conference on ICT in Education, Research and Industrial Applications. Integration, Harmonization and Knowledge Transfer. Volume II: Workshops, Kyiv, Ukraine, May 14-17, 2018. CEUR Workshop Proceedings. 2014, 116–127. http://ceur-ws.org/Vol-2104/paper_175.pdf (2018). Accessed 30 Sep 2018 19. Casey, M.B.: Speculative Bitcoin Adoption/Price Theory. https://medium.com/@mcasey0827/speculative-bitcoin-adoption-price-theory2eed48ecf7da (2016). Accessed 25 Sep 2018 20. McComb, K.: [2018] Bitcoin Crash: Analysis of 8 Historical Crashes and What’s Next. https://blog.purse.io/bitcoin-crash-e112ee42c0b5 (2018). Accessed 25 Sep 2018 21. Amadeo, K.: Stock Market Corrections Versus Crashes and How to Protect Yourself: How You Can Tell If It’s a Correction or a Crash. https://www.thebalance.com/stock-marketcorrection-3305863 (2018). Accessed 25 Sep 2018 22. Donner, R.V., Small, M., Donges, J.F., Marwan, N., Zou, Y., Xiang, R., Kurths, J.: Recurrence-based time series analysis by means of complex network methods. International Journal of Bifurcation and Chaos. 21(04), 1019–1046 (2011). doi:10.1142/S0218127411029021 23. Lacasa, L., Luque, B., Ballesteros, F., Luque, J., Nuño, J.C.: From time series to complex networks: The visibility graph. Proceedings of the National Academy of Sciences of the United States of America. 105(13), 4972–4975 (2008). doi:10.1073/pnas.0709247105 24. Burnie, A.: Exploring the Interconnectedness of Cryptocurrencies using Correlation Networks. arXiv:1806.06632 [q-fin.CP]. https://arxiv.org/pdf/1806.06632 (2018). Accessed 25 Sep 2018-
dc.description.abstractThis article demonstrates the possibility of constructing indicators of critical and crash phenomena in the volatile market of cryptocurrency. For this purpose, the methods of the theory of complex networks have been used. The possibility of constructing dynamic measures of network complexity behaving in a proper way during actual pre-crash periods has been shown. This fact is used to build predictors of crashes and critical events phenomena on the examples of all the patterns recorded in the time series of the key cryptocurrency Bitcoin, the effectiveness of the proposed indicators-precursors of these falls has been identified.uk
dc.language.isoenuk
dc.subjectcryptocurrencyuk
dc.subjectBitcoinuk
dc.subjectcomplex systemuk
dc.subjectcomplex networksuk
dc.subjectmeasures of complexityuk
dc.subjectcrashuk
dc.subjectcritical eventsuk
dc.subjectindicator-precursoruk
dc.titleComplex network precursors of crashes and critical events in the cryptocurrency marketuk
dc.typeArticleuk
Розташовується у зібраннях:Кафедра інформатики та прикладної математики

Файли цього матеріалу:
Файл Опис РозмірФормат 
paper02.pdfArticle799.3 kBAdobe PDFПереглянути/Відкрити


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