Navigating Bitcoin Panic-Selling using Linear Approach
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Abstract
COVID-19 affects significant human activity around the globe, including Bitcoin prices. The Bitcoin price is well known for its volatility, so it is not a big shocker when the panic-selling occurs during the pandemic. However, the mechanism to cope with these breakouts, especially the bearish one, is contentious. The experts give numerous pieces of advice with different conclusions in the end. It is also the same with Machine Learning. Various kernels show different results regarding how the price will move. It depends on the window size, how the data is being preprocessed, and the algorithm used. This paper inspects the best combination that various machine learning can offer with a linear approach to navigate the price prediction based on its depth interval, window size until the algorithms themselves. This paper also proposed a new approach to seeing the prediction range called s-steps ahead prediction using a linear model. The result shows that simple machine learning can herd 99.715% profit even during the bearish breakout.
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How to Cite
[1]
A. Prasetiadi, “Navigating Bitcoin Panic-Selling using Linear Approach”, INFOTEL, vol. 12, no. 4, pp. 141-150, Nov. 2020.
Section
Informatics
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[8] Johannsen, Kolja. "Dead Cat Bounce-Demand Reversal Following the Bursting of a Bubble." Available at SSRN 2961304 (2016).
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[23] Godin, Benoît. "The linear model of innovation: The historical construction of an analytical framework." Science, Technology, & Human Values 31.6 (2006): 639-667.
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[33] Exterkate, Peter, et al. "Nonlinear forecasting with many predictors using kernel ridge regression." International Journal of Forecasting 32.3 (2016): 736-753.
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