Refine
Document Type
- Article (2)
- Working Paper (1)
Language
- English (3)
Has Fulltext
- yes (3)
Is part of the Bibliography
- no (3)
Keywords
- Social media (3) (remove)
The meme stock phenomenon has yet to be explored. In this note, we provide evidence that these stocks display common stylized facts for the dynamics of price, trading volume, and social media activity. Using a regime-switching cointegration model, we identify the meme stock “mementum” which exhibits a different characterization compared to other stocks with high volumes of activity (persistent and not) on social media. Finally, we show that mementum is significant and positively related to the stock’s returns. Understanding these properties helps investors and market authorities in their decisions.
No more technology? A TPACK-survey for pre-service teachers with social media in the digital world
(2023)
In the digital age, social media are integrated into everyday life. To include corresponding topics of the digital world in the classroom, future teachers require specific knowledge and abilities. The extent to which these prerequisites are connected to technology, however, needs to be reevaluated in light of social media's ubiquitous nature. Through adopting the TPACK model for an exemplary topic of the digital world, constructions of space in Geography education, a self-evaluation survey instrument for pre-service teachers is compiled and validated (n = 364); social media are conceptualized as an aspect of technological knowledge. Confirmatory factor analysis confirms that the TPACK model is appropriate for the data, as fit-indices show favorable results. A transformative view of the model is supported. Correlations among all constructs exist, endorsing previous studies’ findings on the difficulties in distinguishing the TPACK knowledge constructs. Technological knowledge, noticeably, displays comparatively low correlations with the other knowledge constructs. This result is contrary to previous studies on TPACK and social media, as well as the relation of TPACK to technological knowledge. Albeit these results are not generalizable for all digital world content in pre-service teacher education, this study, by way of example, contributes to a debate on the conceptualization of technological knowledge when introducing phenomena of the digital world that are related to social media through the TPACK model. Additionally, this study advances research in the area of embedding pre-service teacher education with social media in domain-specific pedagogies.
We focus on the role of social media as a high-frequency, unfiltered mass information transmission channel and how its use for government communication affects the aggregate stock markets. To measure this effect, we concentrate on one of the most prominent Twitter users, the 45th President of the United States, Donald J. Trump. We analyze around 1,400 of his tweets related to the US economy and classify them by topic and textual sentiment using machine learning algorithms. We investigate whether the tweets contain relevant information for financial markets, i.e. whether they affect market returns, volatility, and trading volumes. Using high-frequency data, we find that Trump’s tweets are most often a reaction to pre-existing market trends and therefore do not provide material new information that would influence prices or trading. We show that past market information can help predict Trump’s decision to tweet about the economy.