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Impact of Google searches and social media on digital assets’ volatility

Author

Listed:
  • Fathin Faizah Said

    (Universiti Kebangsaan Malaysia)

  • Raja Solan Somasuntharam

    (MAHSA University)

  • Mohd Ridzwan Yaakub

    (Universiti Kebangsaan Malaysia)

  • Tamat Sarmidi

    (Universiti Kebangsaan Malaysia)

Abstract

Advanced digitalization and financial technology have of recent times become among the most crucial tools. Data mining and sentiment analysis have revealed the importance of digitalization in modern times. This study examines the influence of Google search activity on the volatility of digital assets. We analyzed six digital asset prices for Bitcoin, Bitcoin Cash, Ethereum, Ethereum Classic, Litecoin, and Ripple from the Coinmarketcap database. We used tweets on Twitter to survey users’ sentiment by using the Twitter search Application Programming Interface and Google trend search from web searches, news searches, and YouTube searches data using RStudio software. The study spanned 1 September 2019 to 31 January 2020 and employed the Vector Autoregression (VAR) approach for analysis. The VAR estimation revealed that Google search variables have significantly influenced the volatility of Bitcoin, Ethereum, Litecoin, and Ripple, as supported by the Granger causality test and impulse response function. The results of this study could be useful for investors and policymakers in drawing up strategies to reduce market volatility. These results should thus be useful to investors in developing profitable investment strategies to mitigate the impact of market turbulence.

Suggested Citation

  • Fathin Faizah Said & Raja Solan Somasuntharam & Mohd Ridzwan Yaakub & Tamat Sarmidi, 2023. "Impact of Google searches and social media on digital assets’ volatility," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    • Handle: RePEc:pal:palcom:v:10:y:2023:i:1:d:10.1057_s41599-023-02400-8
    DOI: 10.1057/s41599-023-02400-8
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    1. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    2. Avanidhar Subrahmanyam, 2008. "Behavioural Finance: A Review and Synthesis," European Financial Management, European Financial Management Association, vol. 14(1), pages 12-29, January.
    3. Malcolm Baker & Jeffrey Wurgler, 2006. "Investor Sentiment and the Cross‐Section of Stock Returns," Journal of Finance, American Finance Association, vol. 61(4), pages 1645-1680, August.
    4. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    5. Chien-Yun Chang & Chien-Chien Lo & Jui-Chang Cheng & Tzer-Long Chen & Liang-Yun Chi & Chih-Cheng Chen, 2021. "Relationship between Bitcoin Exchange Rate and Other Financial Indexes in Time Series," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-9, April.
    6. Bagher ASGARNEZHAD NOURI & Samira MOTAMEDI & Milad SOLTANI, 2017. "Empirical Analysis Of The Financial Behavior Of Investors With Brand Approach (Case Study: Tehran Stock Exchange)," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, vol. 64(1), pages 97-121, March.
    7. Lee A. Smales, 2022. "Investor attention and cryptocurrency price crash risk: a quantile regression approach," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 39(3), pages 490-505, February.
    8. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    9. Ding Ding & Chong Guan & Calvin M. L. Chan & Wenting Liu, 2020. "Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-21, December.
    10. Sajjad Faraji Dizaji, 2019. "Trade openness, political institutions, and military spending (evidence from lifting Iran’s sanctions)," Empirical Economics, Springer, vol. 57(6), pages 2013-2041, December.
    11. Abhinava Tripathi & Alok Dixit & Vipul, 2022. "Liquidity commonality in the cryptocurrency market," Applied Economics, Taylor & Francis Journals, vol. 54(15), pages 1727-1741, March.
    12. Young Bin Kim & Jurim Lee & Nuri Park & Jaegul Choo & Jong-Hyun Kim & Chang Hun Kim, 2017. "When Bitcoin encounters information in an online forum: Using text mining to analyse user opinions and predict value fluctuation," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    13. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
    14. Philippas, Dionisis & Rjiba, Hatem & Guesmi, Khaled & Goutte, Stéphane, 2019. "Media attention and Bitcoin prices," Finance Research Letters, Elsevier, vol. 30(C), pages 37-43.
    15. Young Bin Kim & Jun Gi Kim & Wook Kim & Jae Ho Im & Tae Hyeong Kim & Shin Jin Kang & Chang Hun Kim, 2016. "Predicting Fluctuations in Cryptocurrency Transactions Based on User Comments and Replies," PLOS ONE, Public Library of Science, vol. 11(8), pages 1-17, August.
    16. Hasan, Md. Tanvir, 2022. "The sum of all SCARES COVID-19 sentiment and asset return," The Quarterly Review of Economics and Finance, Elsevier, vol. 86(C), pages 332-346.
    17. Kraaijeveld, Olivier & De Smedt, Johannes, 2020. "The predictive power of public Twitter sentiment for forecasting cryptocurrency prices," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 65(C).
    18. Timm O. Sprenger & Andranik Tumasjan & Philipp G. Sandner & Isabell M. Welpe, 2014. "Tweets and Trades: the Information Content of Stock Microblogs," European Financial Management, European Financial Management Association, vol. 20(5), pages 926-957, November.
    19. Bourghelle, David & Jawadi, Fredj & Rozin, Philippe, 2022. "Do collective emotions drive bitcoin volatility? A triple regime-switching vector approach," Journal of Economic Behavior & Organization, Elsevier, vol. 196(C), pages 294-306.
    20. Christian Pinto-Gutiérrez & Sandra Gaitán & Diego Jaramillo & Simón Velasquez, 2022. "The NFT Hype: What Draws Attention to Non-Fungible Tokens?," Mathematics, MDPI, vol. 10(3), pages 1-13, January.
    21. Lars Steinert & Christian Herff, 2018. "Predicting altcoin returns using social media," PLOS ONE, Public Library of Science, vol. 13(12), pages 1-12, December.
    22. Stephanos Papadamou & Alexandros Koulis & Constantinos Kyriakopoulos & Athanasios P. Fassas, 2022. "Cannabis Stocks Returns: The Role of Liquidity and Investors’ Attention via Google Metrics," IJFS, MDPI, vol. 10(1), pages 1-11, January.
    23. Xun Zhang & Fengbin Lu & Rui Tao & Shouyang Wang, 2021. "The time-varying causal relationship between the Bitcoin market and internet attention," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-19, December.
    24. Conghui Chen & Lanlan Liu & Ningru Zhao, 2020. "Fear Sentiment, Uncertainty, and Bitcoin Price Dynamics: The Case of COVID-19," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 56(10), pages 2298-2309, August.
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