The predictive power of public Twitter sentiment for forecasting cryptocurrency prices

Abstract

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 specically for nancial 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-specic lexicon-based sentiment analysis approach, nancial data and bilateral Grangercausality 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 rst to cover the predictive power of Twitter sentiment in the setting of multiple cryptocurrencies and to explore the presence of cryptocurrency-related Twitter bots.

Dynamic impact of negative public sentiment on agricultural product prices during COVID-19

“…Emotions guide people's thoughts and actions ( Wani et al, 2018 ), with most research on the relationships between emotions and market prices having been focused on the financial market. For example, Strauβ et al (2016) used the Granger causality test to study the relationships between the emotions in Dutch newspaper articles and stock market prices, and found that negative emotions better reflected the stock market trends, Kraaijeveld and De Smedt (2020) also used the Granger causality test to determine whether Twitter emotions predicted bitcoin, Li et al (2020b) concluded that the direct measurement of investor sentiment constructed by leveraging user generated messages and text mining methods had some predictive power in the Chinese stock market, and Li et al (2020a) improved the accuracy of stock price predictions by building a new stock prediction system that combined technical stock price indicators and the sentiments expressed in news articles. The sentiment influences on agricultural market price behaviors have also been examined; for example, Hassouneh et al (2012) developed an avian influenza food panic information index to analyze the impact of the avian influenza epidemic on vertical poultry prices in Egypt, Chen et al (2018) calculated the positive and negative emotional tendency values on social networks and tested the Granger causality between the emotions and vegetable prices, and Zeng et al (2019) used a TVP-VAR model to study the impact of the media reported negative emotions on agricultural product price fluctuations, and the time, region, and product impact differences.…”

The psychology of cryptocurrency trading: Risk and protective factors

“…Like speculative stocks, prices can shift rapidly overnight due to sentiment changes, celebrity endorsements, or single comments and their popularity on platforms such as Reddit. Although there is evidence that price rises in crypto stocks correlate with Twitter activity and have some predictability (see Kraaijeveld & De Smedt, 2020), many of these other extraneous factors are unpredictable and therefore (like an unexpected run by a horse) has a strong element of randomness or chance.…”

Deep Learning-Based Cryptocurrency Price Prediction Scheme With Inter-Dependent Relations

“…Rothman et al [55] used videos and posts on YouTube, Facebook, Telegram, and Reddit for sentimental analysis of cryptocurrencies. Kraaijeveld et al [56] analyzed public Twitter sentiment, including Twitter bots, to predict the prices of nine different cryptocurrencies, and Zhang et al [57] examined the relationship between investor attention and cryptocurrencies using Google Trends. Burggraf et al [58] examined the relationship between investor sentiment on Bitcoin return by considering householdlevel and market-level sentiment using Google's search engine.…”