Understanding the Impact of Celebrity Endorsements on the Artificial Growth of Cryptocurrencies Using a Multi-Dimensional Latent Class Analysis and a Software-Based Approach (Year 3)

Overview

In recent years, the intersection of celebrity endorsements and the cryptocurrency market has been a focal point of both interest and concern. The creation of over 10,000 distinct virtual assets and the meteoric rise in popularity of digital currencies, especially Bitcoin, has created a dynamic environment where social and psychological variables impact market dynamics. Even though these endorsements may help advance the goals of developers and particular currencies, the occurrence of celebrity endorsements has sparked serious concerns. This project expands on the research conducted in the previous two years by exploring the intricate connection between cryptocurrency purchases and celebrity endorsements. A three-dimensional multi-level latent class analysis has been employed using sophisticated statistical methods to investigate the complex relationships among Myers-Briggs personality sub-indicators, purchase odds, and conformity. This strategy seeks to identify the psychological underpinnings that motivate people to invest in cryptocurrencies after seeing celebrity endorsements. To identify possible pump-and-dump activity on social media sites, especially Twitter, a program has been created. By utilizing criteria like the ratio of likes to comments, the quantity of followers, and additional engagement markers, this software offers a quantitative evaluation of the legitimacy of price spikes in the cryptocurrency market. With the intent to detect and lessen the effects of possible market manipulation and false gain, this research provides insight into the interplay of celebrity endorsements and cryptocurrencies, information that could become crucial within our rapidly evolving financial landscape.

Competition history

• ISEF 2024 Behavioral and Social Sciences · Entry BEHA001 · Los Angeles, California, United States

Resources

• ISEF abstract page ↗

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