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Contribution Details
Type | Master's Thesis |
Scope | Discipline-based scholarship |
Title | Sentiment Analysis of Cryptocurrencies and Technology Stocks - An Empirical Comparison |
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Institution | University of Zurich |
Faculty | Faculty of Business, Economics and Informatics |
Number of Pages | 168 |
Date | 2021 |
Abstract Text | This master's thesis aims to analyse whether the sentiment expressed on Twitter has an impact on the returns of individual assets. A dataset of approximately 17 million tweets on five technology stocks and five cryptocurrencies is studied over a period of 26 months. The sentiment is assessed daily based on four different models. Among them are the two lexicon-based approaches VADER and Loughran-McDonald as well as a Naive Bayes classifier and finBERT, both of which are based on machine learning techniques. Granger causality tests are conducted to investigate whether the sentiment indices can improve the forecast of returns. Furthermore, a sentiment based trading strategy is implemented and compared to a buy-and-hold strategy as a benchmark. The main findings of the analysis are that a sentiment analysis can be beneficial for the stocks of Facebook and Tesla, as well as for the cryptocurrencies Bictoin and Binance Coin in some scenarios. Overall, however, a stronger Granger causal relationship is found in the opposite direction for most of the assets. The VADER and finBERT models perform better than the other two methods in sentiment analysis. A minor outperformance against the benchmark can be achieved in one case each for Bitcoin and Ethereum. However, in general the buy-and-hold strategy outperforms the implemented trading strategy considerably due to the large number of trading signals, resulting in high transaction costs, and the significant positive developments after the price drops caused by the Corona pandemic. |
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