Abstract
In this paper, we present a set of experiments on predicting the rise or fall of a cryptocurrency using machine learning algorithms and sentiment analysis of the afferent media (online press mainly). The machine learning part is using the data of the currencies (the prices at a specific time) to predict in a mathematical sense. The sentiment analysis of the media (articles about a cryptocurrency) will influence the mathematical prediction, depending on the feeling created around the currency. The study can be useful for entrepreneurs, investors, and normal users, to give them a clue on how to invest. Furthermore, the study is intended for research regarding natural language processing and human psychology (deducting the influence of masses through media) and also in pattern recognition.
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Notes
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Starting with Eugene Fama’s PhD (1965), EMH becoming one of the most known theories in financial economics that confirm the connection between prices and public information.
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Cryptolization.com.
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Coinmarketcap.com.
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References
Barber, B.M., Odean, T.: All that glitters: the effect of attention and news on the buying behavior of individual and institutional investors. In: Review of Financial Studies, 21, 785–818 (2008)
Bariviera, A.F., Zunino, L., Guercio, M.B., Martinez, L.B., Rosso, O.A.: Revisiting the European sovereign bonds with a permutation-information- theory approach. Eur. Phys. J. B. 86, 110 (2014). https://doi.org/10.1140/epjb/e2013-40660-7
Clarke, J., Jandik, T., Mandelker, G.: The efficient markets hypothesis. In: Robert C. ARFFA, ed. Expert Financial Planning: Investment Strategies from Industry Leaders. Wiley, New York Chapter 9, pp. 126–141 (2001)
Dai, Y., Zhang, Y.: Machine Learning in Stock Price Trend Forecasting. Stanford University (2013)
Daniel, K., Hirshleifer, D., Subrahmanyam, A.: Investor psychology and security market under- and overreactions. In: Journal of Finance, 53, 1839–1885 (1998)
Fama, E.: The behavior of stock market prices. J. Bus. 38, 34–105 (1965). https://doi.org/10.1086/294743
Feller, W.: Martingales. In: An Introduction to Probability Theory and Its Applications, Vol. 2, Wiley, New York, pp. 210-215 (1971)
Gîfu, D., Cristea, D.: Public discourse semantics. a method of anticipating economic crisis presented at the exploratory workshop on intelligent decision support systems for crisis management, 8–12 May 2012, Oradea, Romania. In: International Journal of Computers, Communications and Control, see, I. Dzitac, F.G. Filip, M.-J. Manolescu (eds.), vol. 7/5, Agora University Editing House, pp. 829–836 (2012)
Granger, C.W.J.: Forecasting stock market prices: lessons for forecasters. In: International Journal of Forecasting 8, North-Holland, pp. 3–13 (1992)
Hilbert, A., Jacobs, H., Müller, S.: Media makes momentum. Review of Financial Studies 27(12), 3467–3501 (2014)
Hou, K., Peng, L., Xiong, W.: A Tale of Two Anomalies: The Implications of Investor Attention for Price and Earnings Momentum, SSRN 976394 (2009)
Khaidem, L., Saha, S., Dey, S. R.: Predicting the direction of stock market prices using random forest. In: Applied Mathematical Finance, pp. 1–20 (2016)
Marwala, T.: Impact of Artificial Intelligence on Economic Theory – via arXiv.org (2015)
Marwala, T., Hurwitz, E.: Artificial Intelligence and Economic Theory: Skynet in the Market. Springer, London (2017). https://doi.org/10.1007/978-3-319-66104-9
Mittal, A., Goel, A.: Stock prediction using twitter sentiment analysis. Stanford University, CS229 (2012)
Nelson, R.: Prophecy: A History of the Future - The Rex Research Civilization Kit (2000). http://www.rexresearch.com/prophist/phfcon.htm
Nunno, L.: Stock Market Price Prediction Using Linear and Polynomial Regression Models (2017)
Spierdijk, L., Bikker, J.A.: Mean Reversion in Stock Prices: Implications for Long-Term Investors (2012)
Zunino, L., Bariviera, A.F., Guercio, M.B., Martinez, L.B., Rosso, O.A.: On the efficiency of sovereign bond markets. Phys. A Stat. Mech. Appl. 391, 4342–4349 (2012). https://doi.org/10.1016/j.physa.2012.04.009
Xin, Y.: Linear Regression Analysis: Theory and Computing (2009)
Acknowledgments
This survey was published with the support of the grant of the Romanian National Authority for Scientific Research and Innovation, CNCS/CCCDI – UEFISCDI, project number PN-III-P2–2.1-BG-2016–0390 and by a grant of the Romanian Ministry of Research and Innovation CCCDI-UEFISCDI, project number PN-III-P1–1.2-PCCDI-2017–0818 within PNCDI III.
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Chelmuş, R., Gîfu, D., Iftene, A. (2023). Prediction of Cryptocurrency Market. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2018. Lecture Notes in Computer Science, vol 13396. Springer, Cham. https://doi.org/10.1007/978-3-031-23793-5_2
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