ScholarWorks@중앙대학교

초록

Meme stocks, driven by viral social media trends, have added new complexities to financial markets. Prior studies have explored meme stock price dynamics and investor sentiment, but the interplay between social media activity and market movements, along with the structural and linguistic features of online discussions, remains understudied. To address this, we integrate econometric and NLP-based techniques, combining correlation analysis, Granger causality testing, BSADF-based bubble detection, and textual analysis. Our results reveal a strong correlation between trading volume and social media engagement, with Granger causality confirming a feedback loop between market fluctuations and online discussions. BSADF analysis demonstrates that social media-based detection complements price-based methods by identifying explosive periods they may miss. Additionally, network analysis indicates that meme stock discussions exhibit distinct structural patterns, while linguistic analysis highlights unique word choices and emoji usage. Sentiment analysis shows that bullish sentiment dominates during speculative surges, reinforcing the emotionally driven nature of meme stock trading. These findings provide investors with a complementary tool for risk assessment by integrating sentiment with traditional market indicators, while helping regulators monitor online sentiment to identify early signs of speculative excess and market instability. © 2013 IEEE.

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