This thesis investigates the effect of sentiment on cryptocurrencies, and it aims to contribute to the literature on cryptocurrencies by extending the methods that are used in analyzing the impact of investor emotions on price development. Using extensive datasets and daily time-series-based variables, the study compares sentiment scores with financial metrics. By analyzing this relationship, the paper finds valuable information for companies, investors, and researchers interested in understanding the effect of sentiment on crypto. The relationship between sentiment and cryptocurrency prices is investigated by analyzing over 5 million tweets and price data of Bitcoin, Ethereum, and Cardano over a four-year period. Using the VADER sentiment tool, the study measures Twitter sentiment and tests the relationship between sentiment and returns using three different tools: Pearson Correlation, Cointegration, and Granger Causality. The study finds an overall positive sentiment for all the cryptocurrencies and establishes cointegration between all the cryptocurrencies and their sentiment, indicating a long-term relationship between the variables. However, the study only finds a Granger-Causing relationship of returns to sentiment for Bitcoin. The authors conclude that there is a definite long-term relation between sentiment and cryptocurrency price development, which is consistent with findings from previous literature. However, the direction of this relationship between the two variables remains inconclusive.
- Data downloader with Twitter API
- Data converter
- Vader sentiment analysis
- Statistical analysis for ETH