"The Effect of News on Daily Bitcoin Returns A dictionary-based sentiment analysis of market efficiency of the Bitcoin market"

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Since Bitcoin gained attention of the public and media, its prices have fluctuated enormously. Literature is inconclusive about Bitcoins characteristics regarding value determinants, causes of volatility and market efficiency. This thesis focusses on the latter and studies the effect of news sentiment on Bitcoin price returns, and looks how this connects with Bitcoins market efficiency. In a sample period from 2011 until the first quarter of 2019 a dictionary-based sentiment analysis was used to score the news on a scale from positive (+1) to negative (-1), neutral being 0. Via regression and VAR-Granger analysis no evidence was found that the sentiment of the news from leading international news providers has effect on Bitcoin returns, neither positive or negative. The VAR model proved that previous Bitcoin returns affect next day’s returns. Both findings suggests that the Bitcoin market, contrary to the expectations, is efficient, which signals that the Bitcoin market is becoming more mature. This thesis carefully suggests that Bitcoin traders tend to be loss averse. The most important finding is that negative news sentiment is caused by previous Bitcoin returns and previous news sentiment, both positive and negative. Keywords: Bitcoin, News, Dictionary-based Sentiment Analysis, Market Efficiency
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