Abstract
Background: There is a case to be made that the widely popular and highly valued “Bitcoin” (and other significant cryptocurrencies) has become synonymous with blockchain for many retail investors and other non-informed individuals. This study attempts to answer two important research questions in this space. First, the study aims to understand if companies leverage this proximity in technological awareness of Bitcoin and blockchain to attract more investors and users by riding the Bitcoin wave and strategically timing the disclosures. Second, we aim to compute the value of the confounding effect.
Method: To answer these questions, we collected over 4000 blockchain-related announcements from the top 30 NASDAQ-listed firms over the past five years. All announcements are analyzed using text analytics techniques to identify the topic, tone, and complexity. An event study approach adopting a Fama-French four-factor model is developed to detect whether any changes in the market-wide abnormal returns surrounding Bitcoin events influence the company's performance. The relationship between the announcement texts and the abnormal returns is then computed and analyzed.
Results: The results evidence a substantial impact of Bitcoin market returns on the abnormal return instances. Further, it is also observed that strategically framing the firm disclosures concerning blockchain announcements has a significant impact on the market returns.
Conclusion: This study contributes to the literature on digital business strategies within the emerging purview of cryptocurrency networks. At a practical level, the study aims to alert "not-so-well-informed" investors about the possible misconception of Bitcoin performance as a direct driver of the performance of the technological companies making blockchain announcements.
Recommended Citation
Puthineedi, Venu Bhaskar; Eachempati, Prajwal; Jha, Ashish Kumar; and Srivastava, Praveen Ranjan, "An Event Study Approach to Analyze the Confounding Nature of Bitcoin on Blockchain Disclosures" (2024). PAJAIS Preprints (Forthcoming). 31.
https://aisel.aisnet.org/pajais_preprints/31