Trading on Emotions: The Dual regimes of Meme Stocks Driven by Social Media Sentiment

Authors

  • Allan Wang Cranbrook Kingswood

DOI:

https://doi.org/10.47611/jsrhs.v14i1.8677

Keywords:

GameStop, Meme Stocks, WallStreetBets, Sentiment Analysis, Behavioral Finance, Social Media Influence

Abstract

GameStop serves as a striking example of a recent abnormal market phenomenon, where the collective actions of social media users, particularly in the WallStreetBets community on Reddit, have exerted a significant influence on stock prices, leading to phenomenon of "Meme Stocks." This paper introduces the Emotion Regime Switching-VAR methodology to explore the extent to which sentiment from WallStreetBets affects GameStop's price movements. The results reveal that while social media sentiment significantly impacts the market, this influence is not uniform. The market alternates between rational and emotional states, with social media acting as a catalyst behind these shifts. Notably, during emotional states, the market displays heightened volatility and a more substantial effect of sentiment in driving stock prices.

 

Downloads

Download data is not yet available.

References or Bibliography

Li, T., Chen, H., Liu, W., Yu, G., & Yu, Y. (2023). Understanding the role of social media sentiment in identifying irrational herding behavior in the stock market. International Review of Economics & Finance.

Costola, M., Iacopini, M., & Santagiustina, C. R. (2021). On the "mementum" of meme stocks. Economics Letters, 207, 110021. https://doi.org/10.1016/j.econlet.2021.110021

Birru, J., & Young, T. (2022). Sentiment and uncertainty. Journal of Financial Economics, 146(3), 1148-1169. https://doi.org/10.1016/j.jfineco.2022.05.005

Ur Rehman, M., Raheem, I.D., Al Rababa'a, A., Ahmad, N., & Vo, X.V. (2022). Reassessing the Predictability of the Investor Sentiments on US Stocks: The Role of Uncertainty and Risks. Journal of Behavioral Finance, 24, 450 - 465.

Wessels, D., Goedhart, M.H., & Koller, T. (2005). What really drives the market. MIT Sloan Management Review, 47, 21-23.

Shiller, R. J. (2011, July). Debt and delusion. Project Syndicate. Retrieved July 19, 2024, from https://www.project-syndicate.org/commentary/debt-and-delusion

Shiller, R. J. (2020). Narrative economics : how stories go viral & drive major economic events, with a new preface by the author. Princeton University Press.

Foucault, T., Sraer, D., & Thesmar, D. (2009). Individual investors and volatility. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1105470

Barber, B. M., & Odean, T. (2005). All that glitters: The effect of attention and news on the buying behavior of individual and institutional investors. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.460660

Aloosh, A., Ouzan, S., & Shahzad, S. J. H. (2022). Bubbles across meme stocks and cryptocurrencies. Finance Research Letters, 49, 103155. https://doi.org/10.1016/j.frl.2022.103155

Gao, B., & Liu, X. (2020). Intraday sentiment and market returns. International Review of Economics & Finance, 69, 48-62. https://doi.org/10.1016/j.iref.2020.03.010

Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. The Journal of Finance, 25(2), 383–417. https://doi.org/10.2307/2325486

Bechara, A. (2004). The role of emotion in decision-making: Evidence from neurological patients with orbitofrontal damage. Brain and Cognition, 55, 30-40.

Lerner, J.S., Li, Y., Valdesolo, P., & Kassam, K.S. (2015). Emotion and decision making. Annual review of psychology, 66, 799-823 .

Zhang, W., Wang, P., Li, X., & Shen, D. (2018a). Twitter's daily happiness sentiment and international stock returns: Evidence from linear and nonlinear causality tests. Journal of Behavioral and Experimental Finance, 18, 50–53. https://doi.org/10.1016/j.jbef.2018.01.005

Kaplanski, G., & Levy, H. (2010). Sentiment and stock prices: The case of aviation disasters. Journal of Financial Economics, 95(2), 174–201. https://doi.org/10.1016/j.jfineco.2009.10.002

De Long, J. B., Shleifer, A., Summers, L. H., & Waldmann, R. J. (1990). Noise trader risk in financial markets. Journal of Political Economy, 98(4), 703-738.

Chung, S.-L., & Yeh, C.-Y. (2009). Investor sentiment, regimes and stock returns. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1342588

Hamilton, J. D. (2010). Regime switching models. Macroeconometrics and Time Series Analysis, 202-209. https://doi.org/10.1057/9780230280830_23

Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357. https://doi.org/10.2307/1912559

Brown, G. (1999). Volatility, Sentiment, and Noise Traders. Financial Analysts Journal, 55, 82-90.

Perez-Quiros, G., & Timmermann, A. (2000). Firm Size and Cyclical Variations in Stock Returns. The Journal of Finance, 55(3), 1229–1262. http://www.jstor.org/stable/222451

Gray, S. F. (1996). Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics, 42(1), 27-62. https://doi.org/10.1016/0304-405x(96)00875-6

Pedersen, L. H. (2022). Game on: Social networks and markets. Journal of Financial Economics, 146(3), 1097-1119. https://doi.org/10.1016/j.jfineco.2022.05.002

Published

02-28-2025

How to Cite

Wang, A. (2025). Trading on Emotions: The Dual regimes of Meme Stocks Driven by Social Media Sentiment. Journal of Student Research, 14(1). https://doi.org/10.47611/jsrhs.v14i1.8677

Issue

Section

HS Research Articles