Analyzing the Interaction between Tweet Sentiments and Price Volatility of Cryptocurrencies

Abstract

With growing interest toward investments in the cryptocurrency market, prediction of the volatility of the price increasingly becomes important. Given the popularity of social media activity to reflect market trends in recent years, sentiment analysis has been recognized as a great contributing factor to predict financial markets. Using a sample of Bitcoin and Ethereum trade data, this study intends to provide insights on the association between twitter activity about cryptocurrencies and fluctuations of their price. To this end, we implement regression analysis alongside Vector Autoregression method to examine to what extent sentiment-related measures are capable of explaining the volatility of the prices of cryptocurrencies and whether the mutual influence of sentiment and volatility improves the accuracy of the model. Results indicate that the accuracy of predictions vary across the two tested cryptocurrencies, and also two different lexicon approaches used to calculate sentiment scores.