Methods of forecasting the prices of cryptocurrency on the financial markets
Abstract
The article describes the problem of forecasting prices of cryptocurrencies at the financial markets. Methods for analyzing and forecasting prices of cryptocurrencies at the financial markets are considered in detail. A trend indicator – moving averages – is considered as an auxiliary tool for technical analysis that helps to analyze and forecast prices of cryptocurrencies at the financial markets. During the study there were analyzed several methods of different categories, namely: SMA (Simple Moving Average), EMA (Exponential Moving Average), WMA (Weighted Moving Average). For analyzing moving averages, there were conducted the analysis, based on mean-square deviation together with the standard graphic analysis. The whole process was divided in several stages: a moving average was calculated, based on basic values; based on values of the calculated moving average, there was calculated a mean square deviation; deviation with the least numerical value was chosen among the massive of deviations. It has been revealed, that SMA has the least value of mean-square deviation, but EMA is the better choice, because EMA is most sensitive among considered moving averages, although an error extent is rater more.
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References
Casey, M. J. (2015). The Age of Cryptocurrency: How Bitcoin and the Blockchain Are Challenging the Global Economic Order. London: St. Martin's Press, 368.
Antonopoulos, A. M. (2017). Mastering Bitcoin: Unlocking Digital Crypto-Currencies. London: O’Reilly Media, 416.
Ivasenko, A. G. (2015). Rynok tsennykh bumag: instrumenty i mekhanizmy funktsionirovaniia. Moscow: Kno– Rus, 272.
Wafi, A. S., Hassan, H., Mabrouk, A. (2015). Fundamental Analysis Models in Financial Markets – Review Study. Procedia Economics and Finance, 30, 939–947. doi: http://doi.org/10.1016/s2212-5671(15)01344-1
Merfi, Dzh. Dzh. (2011). Tekhnicheskii analiz fiuchersnykh rynkov. Teoriia i praktika. Moscow: Alpina Pablisher, 616.
Luther, W. J. (2015). Cryptocurrencies, network effects, and switching costs. Contemporary Economic Policy, 34 (3), 553–571. doi: http://doi.org/10.1111/coep.12151
Dale, O. (2018). Cryptocurrency Trading: Momentum Indicators and Moving Averages. Available at: https://blockonomi.com/moving-averages/
Podkopytnova, D. V., Atanova, E. V. (2017) Primenenie metoda skolziashchikh srednikh na fondovom rynke. Nauchno-prakticheskie issledovaniia, 9 (9), 105–107.
Grafik aktsii APPLE INC. Trendovye indikatory. Available at: https://ru.tradingview.com/chart/
Zinovik, A. (2017). Sravnenie razlichnykh tipov skolziashchikh srednikh v torgovle». Available at: https://www.mql5.com/ru/articles/3791
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