Announcement effects in the cryptocurrency market

ABSTRACT

Cryptocurrencies have gained popularity as new economic investment assets globally in recent years. This study examines market reactions to major news events associated with cryptocurrencies. Abnormal returns as well as cumulative abnormal returns (CARs) around major news announcements, both positive and negative, are investigated for three primary cryptocurrencies: Bitcoin, Ethereum, and Ripple. High abnormal returns are observed on the event day (Day 0), and CARs typically diverge during event windows of (−3, 6) and (0, 6), indicating that the information is not fully reflected in prices immediately after the news events. The CARs that linger for six days after an event suggest that the information flow in the cryptocurrency market is visibly slow. The magnitudes of CARs are larger for negative events than for positive events, implying that the market reaction to negative events is stronger than to positive announcements. The findings of this study may have crucial implications for investors, arbitragers and practitioners as we document evidence of potential trading opportunities for investors who initiate a trading position even after announcements.

KEYWORDS:

• Cryptocurrency
• event study
• trading opportunities
• abnormal returns

JEL CLASSIFICATION:

• E42
• G14

Acknowledgments

We would like to thank the Editor, Dr. David Peel, and anonymous referee for providing crucial suggestions and comments that improved the article substantially. We are also appreciative of the insightful comments from Ali M. Parhizgari, Qiang Kang, Özde Öztekin, and session participants at the 2019 Eastern Finance Association Annual Meeting and 2019 Southwestern Finance Association Annual Meeting. Remaining errors are ours.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ To validate this point, we also used event windows of (−2, 6) and (−1, 6) and found similar results. Such findings are not included in this paper for brevity.

² We also used shorter windows such as (−3, 3) and (0, 3) as robustness checks, and the CAR patterns still hold with similar results.

³ Using the Shapiro-Francia procedure as well as a normality test based on skewness and another based on kurtosis, we also confirm the results from the Shapiro-Wilk procedure. The results from these tests are excluded for brevity.

⁴ T-statistics based on the Kolari and Pynnonen (2011) GRANK test are utilized to evaluate the statistical significance of the results using the 180-day moving average as well as the 60-day moving average, in the same way, we tested the statistical significance of the baseline results presented in Table 4.