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In this work, the level of influence of the posts published by famous people on social networks on the formation of the cryptocurrency exchange rate is investigated. Celebrities who are familiar with the financial industry, especially with the cryptocurrency market, or are somehow connected to a certain cryptocurrency, such as Elon Musk with Dogecoin, are chosen as experts whose influence through social media posts on cryptocurrency rates is examined. This research is conducted based on statistical analysis. Real cryptocurrency exchange rate forecasts for the selected time period and predicted ones for the same period, obtained using three algorithms, are utilized as a dataset. This paper uses methods such as statistical hypotheses regarding the significance of Spearman’s rank correlation coefficient and Pearson’s correlation. It is confirmed that the posts by famous people on social networks significantly affect the exchange rates of cryptocurrencies.
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Tom
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art. no. e150117
Opis fizyczny
Bibliogr. 27 poz., tab.
Twórcy
autor
- Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland
autor
- Faculty of Electrical and Computer Engineering, Cracow University of Technology, Warszawska 24, 31-155 Cracow, Poland
autor
- National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,” Prosp. Peremohy 37, Kyiv, Ukraine
autor
- National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,” Prosp. Peremohy 37, Kyiv, Ukraine
autor
- National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute,” Prosp. Peremohy 37, Kyiv, Ukraine
Bibliografia
- [1] P. Preethi, V. Uma, and A. Kumar, “Temporal Sentiment Analysis and Causal Rules Extraction from Tweets for Event Prediction,” Procedia Comput. Sci., vol. 48, pp. 84–89, 2015, doi: 10.1016/j.procs.2015.04.154.
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- [3] R. Alhajj and J. Rokne, Encyclopedia of Social Network Analysis and Mining, Springer, 2020, p. 2200, doi: 10.1007/978-1-4614-7163-9.
- [4] Y. Dang et al., “An integrated framework for analyzing multilingual content in Web 2.0 social media,” Decis. Support Syst., vol. 61, pp. 126–135, 2014, doi: 10.1016/j.dss.2014.02.004.
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- [6] G. ChengHe, “Dynamic topic modeling of twitter data during the COVID-19 pandemic,” PLoS ONE, vol. 17, no. 5, p. 22, 2022, doi: 10.7910/DVN/YXIAEK.
- [7] M. Chau and J. Xu, “Mining communities and their relationships in blogs: A study of online hate groups,” Int. J. Hum.-Comput. Stud., vol. 65, no. 1, pp. 57–70, 2007.
- [8] D. Karahoca, A. Karahoca, and Ö. Yavuz, “An early warning system approach for the identification of currency crises with data mining techniques,” Neural Comput. Appl., vol. 23, pp. 2471–2479, 2013, doi: 10.1007/s00521-012-1206-9.
- [9] M. Žukovič, “Dynamics of episodic transient correlations in currency exchange rate returns and their predictability,” Open Phys., vol. 10, pp. 615–624, 2012, doi: 10.2478/s11534-011-0120-6.
- [10] O. Gavrilenko, K. Novakivska, and O. Shumeiko, “Selection of the most influential economic factors for forecasting the US dollar exchange rate,” NTU Bull., vol. 54, pp. 26–35, 2022, doi: 10.33744/2308-6645-2022-4-54-026-035.
- [11] O. Gavrylenko, M. Miahkyi, and Y. Zhurakovskyi, “The task of analyzing publications to build a forecast for changes in cryptocurrency rates,” Adapt. Syst. Autom. Control, vol. 2 no. 41, pp. 90–99, 2022, doi: 10.20535/1560-8956.41.2022.271349.
- [12] ARIMA Model – Complete Guide to Time Series Forecasting in Python – Resources [Online] Available: https://www.machinelearningplus.com/%20time-series/arima-model-timeseries-forecasting-python/. (Accessed 05.04.2024).
- [13] R.J. Hyndman, A.B. Koehler, J.K. Ord, and R. D. Snyder, Forecasting with Exponential Smoothing: The State Space Approach. Springer, 2008, p. 362, doi: 10.1007/978-3-540-71918-2.
- [14] Binance cryptocurrency exchange – Resources [Online] Available: https://www.binance.com/ (Accessed 05.04.2024).
- [15] M. Kartashov, Probability, processes, statistics. Kyiv University, 2007, p. 504.
- [16] O. Valenzuela, F. Rojas, L.J. Herrera, H. Pomares, and I. Rojas, Theory and Applications of Time Series Analysis and Forecasting. Springer, 2023, p. 333, doi: 10.1007/978-3-031-14197-3.
- [17] A.D. Helfrick and W.D. Cooper, Modern Electronic Instrumentation and Measurement Techniques. Prentice Hall of India, 2008, p. 460.
- [18] H. Abdi,V. Guillemot, A. Eslami, and D. Beaton, Canonical Correlation Analysis. Springer, 2017, pp. 1–16, doi: 10.1007/978-1-4614-7163-9_110191-1.
- [19] Rank correlation – Resources [Online]Available: https://moodle.znu.edu.ua/ (Accessed 05.04.2024).
- [20] W.J. Ewens and K. Brumberg, Introductory Statistics for Data Analysis. Springer, 2023, p. 273, doi: 10.1007/978-3-031-28189-1.
- [21] B. Blaine, Introductory Applied Statistics. Springer, 2023, p. 190, doi: 10.1007/978-3-031-27741-2.
- [22] O. Gavrilenko et al., “The principle for forming a portfolio of public services based on the analysis of statistical information,” East.-Eur. J. Enterprise Technol., vol. 3, no. 3(117), pp. 57–64, 2022, doi: 10.15587/1729-4061.2022.260136.
- [23] J. Ascorbebeitia, E. Ferreira, and S. Orbe, Testing conditional multivariate rank correlations: the effect of institutional quality on factors influencing competitiveness. Springer, 2022, pp. 931–949, doi: 10.1007/s11749-022-00806-1.
- [24] V. Chymshyr, S. Telenyk, Î.Rolik, and E. Zharikov, “The platform for supporting the life cycle of services in the information systems of information and communication service providers,” Adapt. Syst. Autom. Control, vol. 1 no. 42, pp. 205–226, 2023, doi: 10.20535/1560-8956.42.2023.279172.
- [25] P. Kapler, “The application of the fuzzy rule-based Bayesian algorithm to determine which residential appliances can be considered for the demand response program,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 71, no. 4, p. e146106, 2023, doi: 10.24425/bpasts.2023.146106.
- [26] O. Gavrilenko and M. Myagkyi, “Forecasting the cryptocurrency exchange rate based on the ranking of expert opinions,” Innov. Technol. Sci. Sol. Ind., no. 1, no. 27, pp. 18–25, 2024, doi: 10.30837/ITSSI.2024.27.018.
- [27] G. Nowakowski, S. Telenyk, and Y. Vovk, “Chatbots Lifecycle Support Platform,” 2023 IEEE 12th International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Germany, 2023, pp. 308–319, doi: 10.1109/IDAACS58523.2023.10348794.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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