Fundamental vs. Technical Analysis : News-based Factors and Cryptocurrency Risk Premia

This paper investigates the cross-sectional predictive ability of text-based factors in the cryptocurrency market --an important asset class for retail and institutional investors. We employ Bidirectional Encoder Representations from Transformers (BERT) topic modeling to analyze news articles discussing the top 43 cryptocurrencies by market capitalization. We build text-based factors related to fundamentals and technical trading. We find that pessimism about technical news is positively priced in the cross-section of cryptocurrency returns, while pessimism about fundamental news is negatively priced. These factors provide information over and above existing factor models. Our results demonstrate the importance of considering text-based factors when analyzing cryptocurrency returns