Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

Alexey Yu. Mikhaylov (), Vikas Khare (), Solomon Eghosa Uhunamure (), Tsangyao Chang () and Diana I. Stepanova ()
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Alexey Yu. Mikhaylov: Financial University under the Government of the Russian Federation, Moscow, Russian Federation
Vikas Khare: School of Technology, Management and Engineering NMIMS, Indore, India
Solomon Eghosa Uhunamure: Cape Peninsula University of Technology, Cape Town, South Africa
Tsangyao Chang: Department of Finance, Feng Chia University, Taichung, Taiwan
Diana I. Stepanova: Plekhanov Russian University of Economics, Moscow, Russian Federation

Finansovyj žhurnal — Financial Journal, 2023, issue 4, 123-137

Abstract: The goal of the article is to develop an innovative forecasting approach based on the Random Forest and fuzzy logic models for predicting crypto-asset prices (IFSs, PFSs, q-ROFSs). The baseline forecast horizon is 90 days (additional horizons are 30, 60, 120 and 150 days), which allows to estimate the significance of the chosen features and the impact of time on the forecast accuracy. The paper proposes an optimal data selection approach for the Random Forest and fuzzy logic models to improve the prediction of the daily closing price of Bitcoin, using online social network activity, trading parameters, technical indicators, and data on other cryptocurrencies. This paper utilizes a tree-based machine learning prediction and a fuzzy logic model for Bitcoin. The article attempts to prove that automated Bitcoin forecasting using machine learning algorithms is very effective for the cryptocurrency market. Nevertheless, the latter is characterized by high volatility, significant rate hikes of the most liquid cryptocurrencies (mainly Bitcoin). Therefore, investments in cryptocurrencies, especially long-term ones, involve significant risks. This defines the paper’s significance for investors and regulators. As shown by simulation studies of data selection approaches generalizing the accuracy performance of the Random Forest and fuzzy logic models to real preferences of forecasting, even under significant noise measurements, the proposed selection approach leads to fast convergence of estimates. The accuracy of the model’s results exceed 85.21 on a 90-day time horizon.

Keywords: cryptocurrency; investor behavior; Bitcoin; inflation; Twitter sentiment (search for similar items in EconPapers)
JEL-codes: D53 E31 E44 F21 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:fru:finjrn:230408:p:123-137

DOI: 10.31107/2075-1990-2023-4-123-137

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