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Exploring The Relationship Between Google Trends And Cryptocurrency Metrics

Author

Listed:
  • Ramona ORĂȘTEAN

    (Lucian Blaga University of Sibiu, Romania)

  • Silvia Cristina MĂRGINEAN

    (Lucian Blaga University of Sibiu, Romania)

  • Raluca SAVA

    (Lucian Blaga University of Sibiu, Romania)

Abstract

Bitcoin and Ethereum are the two largest cryptocurrencies in the world by market capitalization and trading volume and the most popular despite high price fluctuations. This paper analyzes the relationship between Bitcoin and Ethereum metrics and the internet search interest on cryptocurrencies. As the literature shows, Google searches signal investor attention and Google Trends has proven useful for nowcasting economic and financial indicators. We aim to find the impact of Google Trends on Bitcoin and Ethereum prices, trading volumes and market capitalization since 2015 and discuss the potential correlations and patterns that may exist between these metrics and Google search interest. Through correlation and time-series analysis, we provide insights into the dynamics of this relationship and its implications for understanding cryptocurrency market behavior. The interest in cryptocurrencies tracked by Google Trends is a good indicator of measuring the social interest in the cryptocurrency market that drives a price movement. On the other hand, the price fluctuations of Bitcoin and Ethereum generate media and social attention and increase the interest in these cryptocurrencies. We also observe a positive effect of Google Trends values on trading volumes. The findings could help investors to understand the cryptocurrencies dynamics and build their trading strategies and could be of special interest to policymakers.

Suggested Citation

  • Ramona ORĂȘTEAN & Silvia Cristina MĂRGINEAN & Raluca SAVA, 2024. "Exploring The Relationship Between Google Trends And Cryptocurrency Metrics," Studies in Business and Economics, Lucian Blaga University of Sibiu, Faculty of Economic Sciences, vol. 19(1), pages 368-379, April.
    • Handle: RePEc:blg:journl:v:19:y:2024:i:1:p:368-379
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    References listed on IDEAS

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    1. Nagao, Shintaro & Takeda, Fumiko & Tanaka, Riku, 2019. "Nowcasting of the U.S. unemployment rate using Google Trends," Finance Research Letters, Elsevier, vol. 30(C), pages 103-109.
    2. Hyunyoung Choi & Hal Varian, 2012. "Predicting the Present with Google Trends," The Economic Record, The Economic Society of Australia, vol. 88(s1), pages 2-9, June.
    3. Urquhart, Andrew, 2018. "What causes the attention of Bitcoin?," Economics Letters, Elsevier, vol. 166(C), pages 40-44.
    4. Wei Zhang & Pengfei Wang, 2020. "Investor attention and the pricing of cryptocurrency market," Evolutionary and Institutional Economics Review, Springer, vol. 17(2), pages 445-468, July.
    5. Jeffrey Chu & Saralees Nadarajah & Stephen Chan, 2015. "Statistical Analysis of the Exchange Rate of Bitcoin," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-27, July.
    6. Götz, Thomas B. & Knetsch, Thomas A., 2019. "Google data in bridge equation models for German GDP," International Journal of Forecasting, Elsevier, vol. 35(1), pages 45-66.
    7. David Garcia & Frank Schweitzer, 2015. "Social signals and algorithmic trading of Bitcoin," Papers 1506.01513, arXiv.org, revised Sep 2015.
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