The predictive power of public Twitter sentiment for forecasting cryptocurrency prices
By Olivier Kraaijeveld, J. Smedt. Journal of international financial markets, institutions, and money, 2020
348 citations recorded by the source corpus.
Cryptocurrencies have become a very popular topic recently, primarily due to their disruptive potential and reports of unprecedented returns. In addition, academics increasingly acknowledge the predictive power of Twitter for a wide variety of events and more specifically for financial markets. This paper studies to what extent public Twitter sentiment can be used to predict price returns for the nine largest cryptocurrencies: Bitcoin, Ethereum, XRP, Bitcoin Cash, EOS, Litecoin, Cardano, Stellar and TRON. By using a cryptocurrency-specific lexicon-based sentiment analysis approach, financial data and bilateral Granger-causality testing, it was found that Twitter sentiment has predictive power for the returns of Bitcoin, Bitcoin Cash and Litecoin. Using a bullishness ratio, predictive power is found for EOS and TRON. Finally, a heuristic approach is developed to discover that at least 1–14% of the obtained Tweets were posted by Twitter “bot” accounts. This paper is the first to cover the predictive power of Twitter sentiment in the setting of multiple cryptocurrencies and to explore the presence of cryptocurrency-related Twitter bots.
Subfields: Economics and Econometrics, Information Systems, Management Science and Operations Research
View Paper: https://www.semanticscholar.org/paper/4ddd97c30493ef75459402f7d73e7070c3539ff4
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Citations in the corpus
- Does Sentiment Impact Cryptocurrency? (2021, background): “…proxies such as Google search trends (Panagiotidis, Stengos, and Vravosinos 2018), Wikipedia search results (Kristoufek 2013), news sentiment (Karalevicius, Degrande, and De Weerdt 2018), Twitter sentiment (Kraaijeveld and De Smedt 2020), and cryptocurrency blog sentiment (Ahn and Kim 2020).”
- A survey on sentiment analysis methods, applications, and challenges (2022, background): “However, investigations which apply Sentiment Analysis towards the area of blockchain technology are still infrequent, and those that do exist, such as work in Kraaijeveld and De Smedt (2020), have employed sentiment analysis to anticipate the value of digital cryptocurrencies.”
- A review on sentiment analysis from social media platforms (2023, result): “We can also mention other papers, like the one focused on polarity in Twitter Singh (2020), the forecast on the opinion of the UK parliament and EU over Brexit Chandio & Sah (2020), or the predictions about the price return of nine crypto-currencies Kraaijeveld & Smedt (2020).”