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Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic

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  • Bouteska, Ahmed
  • Mefteh-Wali, Salma
  • Dang, Trung

Abstract

In this paper, we examine the impact of investor sentiment on Bitcoin returns. Using a large dataset of messages discussed on social media and several financial indicators, we create a sentiment indicator based on computational text analysis and driven by the principal component analysis (PCA) method. We utilize a vector autoregressive analysis and other analytical methods to examine the sentiment index–bitcoin return nexus. Our findings reveal that the sentiment index is a strong predictor of cryptocurrency market returns in the short term. Furthermore, we confirm that during the COVID-19 pandemic, investors' sentiments significantly impacted Bitcoin returns. Our results show that the proposed sentiment index can generate excess returns for investors who utilize it as a return predictor. Our empirical findings suggest important policy implications.

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  • Bouteska, Ahmed & Mefteh-Wali, Salma & Dang, Trung, 2022. "Predictive power of investor sentiment for Bitcoin returns: Evidence from COVID-19 pandemic," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
  • Handle: RePEc:eee:tefoso:v:184:y:2022:i:c:s0040162522005200
    DOI: 10.1016/j.techfore.2022.121999
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    1. Zhao, Lu-Tao & Xing, Yue-Yue & Zhao, Qiu-Rong & Chen, Xue-Hui, 2023. "Dynamic impacts of online investor sentiment on international crude oil prices," Resources Policy, Elsevier, vol. 82(C).
    2. Clark, Ephraim & Lahiani, Amine & Mefteh-Wali, Salma, 2023. "Cryptocurrency return predictability: What is the role of the environment?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    3. Khalfaoui, Rabeh & Mefteh-Wali, Salma & Dogan, Buhari & Ghosh, Sudeshna, 2023. "Extreme spillover effect of COVID-19 pandemic-related news and cryptocurrencies on green bond markets: A quantile connectedness analysis," International Review of Financial Analysis, Elsevier, vol. 86(C).
    4. Adel Benhamed & Ahlem Selma Messai & Ghassen El Montasser, 2023. "On the Determinants of Bitcoin Returns and Volatility: What We Get from Gets?," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    5. Gaies, Brahim & Nakhli, Mohamed Sahbi & Sahut, Jean-Michel & Schweizer, Denis, 2023. "Interactions between investors’ fear and greed sentiment and Bitcoin prices," The North American Journal of Economics and Finance, Elsevier, vol. 67(C).
    6. Ştefan Cristian Gherghina & Liliana Nicoleta Simionescu, 2023. "Exploring the asymmetric effect of COVID-19 pandemic news on the cryptocurrency market: evidence from nonlinear autoregressive distributed lag approach and frequency domain causality," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-58, December.
    7. Clark, Ephraim & Lahiani, Amine & Mefteh-Wali, Salma, 2023. "Cryptocurrency return predictability: What is the role of the environment?," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

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    More about this item

    Keywords

    Behavioral finance; Investor sentiment; Bitcoin; Cryptocurrencies; Textual analysis for sentiment analysis;
    All these keywords.

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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