In Bitcoin We Trust: Social Media Sentiment and Cryptocurrency Returns

PI: Prof. Tse-Chun Lin, HKU

Project Abstract: 

We propose a sentiment-based view on examining investment returns of cryptocurrencies. Cryptocurrency sentiment refers to the emotions and opinions expressed in cryptocurrency- related texts. We investigate whether sentiment-based strategies, especially from social media, generate economically sizable and statistically significant returns, which are not explained by the emerging factor models in the cryptocurrency market. We then explore whether exogenous sentiment shocks affect user growth and if such effect, in turn, contrasts or reinforces the user base feedback effect that underpins blockchain network theories. Blockchain network theories broadly suggest that user network growth positively reinforces itself as existing users are motivated to promote the network and benefit from its growth. We investigate the role of sentiment in the flywheel of user growth and examine if sentiment affects cryptocurrency returns via the channel of user growth. To improve the measurement of the sentiment premium in the cryptocurrency market, we employ three common machine learning methods, which allow us to capture the complicated dependence between different sentiments, which sheds more light on our understanding of how various sentiments affect the cryptocurrency market. Overall, our project aims to provide the first evidence of the nexus between cryptocurrency markets, machine learning methods, and behavioral finance insights.