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Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis

Author

Listed:
  • 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)

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.

Suggested Citation

  • Alexey Yu. Mikhaylov & Vikas Khare & Solomon Eghosa Uhunamure & Tsangyao Chang & Diana I. Stepanova, 2023. "Bitcoin Price Short-term Forecast Using Twitter Sentiment Analysis," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 4, pages 123-137, August.
    • Handle: RePEc:fru:finjrn:230408:p:123-137
    DOI: 10.31107/2075-1990-2023-4-123-137
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    cryptocurrency; investor behavior; Bitcoin; inflation; Twitter sentiment;
    All these keywords.

    JEL classification:

    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • E31 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Price Level; Inflation; Deflation
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • F21 - International Economics - - International Factor Movements and International Business - - - International Investment; Long-Term Capital Movements

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