Gustavo Schwenkler, Santa Clara University
Project Description/Abstract
When large idiosyncratic shocks hit a cryptocurrency, some of its peers experience unusually large returns of the opposite sign. The co-movement is concentrated among peers that are co-mentioned with shocked cryptos in the news and that are listed in the same exchanges as shocked cryptos. It is a form of mis-pricing that vanishes after several weeks, giving rise to predictable returns. We propose a profitable trading strategy that exploits this predictability and explain our results with a slow information processing mechanism. To establish our results, we develop a novel natural language processing technology that identifies crypto peers from news data. Our results highlight the news as a key driver of co-movement among peer assets.
Co-author
Hanna Zheng, Fidelity Investments