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http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4119| Назва: | Complex networks theory and precursors of financial crashes |
| Автори: | Соловйов, Володимир Миколайович Solovieva, Victoria Tuliakova, Anna Hostryk, Alexey Pichl, Lukáš |
| Ключові слова: | crypto index visibility graph complexity measures of financial crashes |
| Дата публікації: | 26-жов-2020 |
| Видавництво: | Arnold Kiv |
| Бібліографічний опис: | Soloviev V. Complex networks theory and precursors of financial crashes [Electronic resource] / Vladimir Soloviev, Victoria Solovieva, Anna Tuliakova, Alexey Hostryk, Lukáš Pichl // 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. 53-67. – Access mode : http://ceur-ws.org/Vol-2713/paper03.pdf |
| Короткий огляд (реферат): | Based on the network paradigm of complexity in the work, a systematic analysis of the dynamics of the largest stock markets in the world and cryptocurrency market has been carried out. According to the algorithms of the visibility graph and recurrence plot, the daily values of stock and crypto indices are converted into a networks and multiplex networks, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. This work is the first to investigate the network properties of the crypto index CCI30 and the multiplex network of key cryptocurrencies. It is shown that some of the spectral and topological characteristics can serve as measures of the complexity of the stock and crypto market, and their specific behaviour in the pre-crisis period is used as indicators- precursors of critical phenomena. |
| Опис: | 1. Avalos-Gaytán, V., Almendral, J.A., Leyva, I., Battiston, F., Nicosia, V., Latora, V., Boccaletti, S.: Emergent explosive synchronization in adaptive complex networks. Physical Review E 97(4), 042301 (2019) 2. Bargigli, L., di Iasio, G., Infante, L., Lillo, F., Pierobon, F.: The multiplex structure of interbank networks. Quantitative Finance 15(4), 673–691 (2015) 3. Bianconi, G.: Interdisciplinary and physics challenges in network theory. EPL 111(5), 56001 (2015) 4. Boccaletti, S., Bianconi, G., Criado, R., del Genio, C.I., Gómez-Gardeñes, J., Romance, M., Sendiña-Nadal, I., Wang, Z., Zanin, M.: The structure and dynamics of multilayer networks. Physics Reports 544(1), 1–122 (2014) 5. Chatzis, S.P., Siakoulis, V., Petropoulos, A., Stavroulakis, E., Vlachogiannakis, N.: Forecasting stock market crisis events using deep and statistical machine learning techniques. Expert Systems with Applications 112, 353–371 (2018). doi: 10.1016/j.eswa.2018.06.032 6. Cheng, F., Kovács, I.A., Barabási, A.L.: Network-based prediction of drug combinations. Nature Communications 10(1), 1197 (2019) 7. Crypto Currency Index CCi30. http://cci30.com (2020). Accessed 17 Aug 2020 8. Derbentsev, V., Matviychuk, A., Soloviev, V.N.: Forecasting of Cryptocurrency Prices Using Machine Learning. In: Pichl, L., Eom, C., Scalas, E., Kaizoji, T. (eds.) Advanced Studies of Financial Technologies and Cryptocurrency Markets, pp. 211–231. Springer, Singapore (2020). doi:10.1007/978-981-15-4498-9_12 9. 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(4), 1019–1046 (2011) 10. Fortunato, S., Bergstrom, C.T., Börner, K., Evans, J.A., Helbing, D., Milojević, S., Petersen, A.M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D., Barabási, A.-L.: Science of science. Science 359(6379), eaao0185 (2018) 11. Kiv, A., Semerikov, S., Soloviev, V., Kibalnyk, L., Danylchuk, H., Matviychuk, A.: Experimental Economics and Machine Learning for Prediction of Emergent Economy Dynamics. CEUR Workshop Proceedings 2422, 1–4 (2019) 12. 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) 13. Lacasa, L., Nicosia, V., Latora, V.: Network structure of multivariate time series. Scientific Reports 5, 15508 (2015) doi:10.1038/srep15508 14. Li, S., Wen, S.: Multiplex Networks of the Guarantee Market: Evidence from China. Complexity 2017, 9781890 (2017). doi:10.1155/2017/9781890 15. List of stock market crashes and bear markets. https://en.wikipedia.org/wiki/List_of_stock_market_crashes_and_bear_markets (2020). Accessed 17 Aug 2020 16. Luque, B., Lacasa, L., Ballesteros, F., Luque, J.: Horizontal visibility graphs: Exact results for random time series. Physical Review E 80(4), 046103 (2009). doi:10.1103/PhysRevE.80.046103 17. Malinetskii, G.G., Akhromeeva, T.S.: Self Organization in Complex Systems and New Problems in the Theory of Measurement. Measurement Techniques 59(6), 577–583 (2016) 18. Markova, O., Semerikov, S., Popel, M.: CoCalc as a learning tool for neural network simulation in the special course “Foundations of Mathematic Informatics”. CEUR Workshop Proceedings 2104, 338–403 (2018) 19. Musmeci, N., Nicosia, V., Aste, T., Di Matteo, T., Latora, V.: The Multiplex Dependency Structure of Financial Markets. Complexity 2017, 9586064 (2017). doi:10.1155/2017/9586064 20. Newman, M.E.J., Barabási, A.L., Watts, D.: The Structure and Dynamics of Networks. Princeton University Press, Princeton (2006) 21. Prigogine, I.R.: Networks society. Sotsiologicheskie Issledovaniya (1), 24–27 (2008) 22. Riolo, M.A., Newman, M.E.J.: Consistency of community structure in complex networks. Physical Review E 101(5), 052306 (2020) 23. Semerikov, S., Chukharev, S., Sakhno, S., Striuk, A., Osadchyi, V., Solovieva, V., Vakaliuk, T., Nechypurenko, P., Bondarenko, O., Danylchuk, H.: Our sustainable coronavirus future. E3S Web of Conferences 166, 00001 (2020). doi:10.1051/e3sconf/202016600001 24. Semerikov, S.O., Teplytskyi, I.O., Yechkalo, Yu.V., Markova, O.M., Soloviev, V.N., Kiv, A.E.: Computer Simulation of Neural Networks Using Spreadsheets: Dr. Anderson, Welcome Back. CEUR Workshop Proceedings 2393, 833–848 (2019) 25. Soloviev, V., Belinskij, A.: Methods of nonlinear dynamics and the construction of cryptocurrency crisis phenomena precursors. CEUR Workshop Proceedings 2104, 116–127 (2018) 26. Soloviev, V., Solovieva, V., Tuliakova, A., Ivanova, M.: Construction of crisis precursors in multiplex networks. Advances in Economics, Business and Management Research 99, 361–366 (2019) doi:10.2991/mdsmes-19.2019.68 27. Soloviev, V., Solovieva, V., Tuliakova, A.: Visibility graphs and precursors of stock crashes. Neuro-Fuzzy Technologies of Modeling in Economy 8, 3–29 (2019). doi:10.33111/nfmte.2019.003 28. Soloviev, V., Tuliakova, A.: Graphodinamical Research Methods for Complexity of Modern Stock Markets. Neuro-Fuzzy Technologies of Modeling in Economy 5, 152–179, (2016) 29. Soloviev, V.N., Belinskiy, A.: Complex Systems Theory and Crashes of Cryptocurrency Market. Communications in Computer and Information Science 1007, 276–297 (2019) 30. Stephen, C.: Dynamic Phase and Group Detection in Pedestrian Crowd Data Using Multiplex Visibility Graphs. Procedia Computer Science 53, 410–419 (2015) 31. Vespignani, A.: Twenty years of network science. Nature 558(7711), 528–529 (2018) 32. Xie, A.: The Ultimate Guide on Cryptocurrency Index Funds. https://www.hodlbot.io/blog/ultimate-guide-on-cryptocurrency-indices (2019). Accessed 25 Oct 2019 33. Yahoo Finance: All Cryptocurrencies Screener. https://finance.yahoo.com/cryptocurrencies (2020). Accessed 17 Aug 2020 34. Yahoo Finance: Stock Market Live, Quotes, Business & Finance News. https://finance.yahoo.com (2020). Accessed 17 Aug 2020 |
| URI (Уніфікований ідентифікатор ресурсу): | http://ceur-ws.org/Vol-2713/paper03.pdf http://elibrary.kdpu.edu.ua/xmlui/handle/123456789/4119 https://doi.org/10.31812/123456789/4119 |
| ISSN: | 1613-0073 |
| Розташовується у зібраннях: | Кафедра інформатики та прикладної математики |
Файли цього матеріалу:
| Файл | Опис | Розмір | Формат | |
|---|---|---|---|---|
| paper03.pdf | article | 5.77 MB | Adobe PDF | Переглянути/Відкрити |
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