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We characterize the high-frequency price crash risk of Bitcoin. Using advanced text-mining techniques, we quantify emotional factors in the cryptocurrency market and investigate whether the proliferation of emotions in the cryptocurrency profession predicts the frequent occurrence of instant market crashes. We find that the cross-sectional dispersion of a catalogue of emotions is positively associated with high-frequency crash risk in the cryptocurrency market. These results are more salient for negative emotions. Differences in negative emotions may increase conflicts within a profession, resulting in idiosyncratic market outcomes.
Ahn et al. (Thu,) studied this question.