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Finfluencers as mood barometers? The influence of social media on financial markets

Whether Seeking Alpha, Reddit or X – social media platforms are widely used today for the dissemination and consumption of financial information. A recent survey by Forbes Advisor even found that almost 80% of young adults have already received financial advice via social media.
[This content is not available in "Englisch" yet] A selfie stick in front of a blue sky.

The growing influence of social media in the financial world presents both opportunities and risks: on the one hand, it democratises access to financial information and enables a wider audience to engage with financial topics and advice; on the other hand, it facilitates potential risks and fraud, such as misinformation and market manipulation. These risks are exacerbated by the anonymity of social networks, lack of regulation and the social dynamics that facilitate the rapid spread of information.

Of particular interest to researchers is crowd sentiment, the collective opinions and emotional states conveyed through social media posts, which often reflect attitudes towards certain assets. Crowd sentiment is widely regarded as an indicator of market sentiment, and due to its predictive power, crowd sentiment has become an important emotional driver for private investors, among others. Companies, on the other hand, need to understand and manage sentiment on social media, as it can have a significant impact on their share value. Finfluencers play an important role in these dynamics – with their online presence, they can influence the financial perspectives and decisions of others as opinion leaders.

The study by WiSo professor Detlef Schoder and WiSo researchers Oliver Rath, Frederic Haase and Jonas Krauß answers the following two questions:

  • Question 1: Can the sentiment of finfluencers predict crowd sentiment in financial social media
  • Question 2: Under what conditions does the sentiment of f influencers have increased predictive power on crowd sentiment in financial social media?

The study analyses 80 million posts on stocks and cryptocurrencies and groups the actors according to their potential in social networks to distinguish between those with influence potential and the broader crowd. The authors create time series of sentiment using transformer-based models for both groups and apply panel vector error correction models (PVECMs) to examine their relationship.

The study's findings contribute to the fields of information systems (IS) and marketing research by examining the role of social media influencers (SMIs) in contexts outside traditional consumer marketing. The study's findings have significant implications for regulators, businesses, social media platforms, and investors, supporting the development of regulatory frameworks, marketing strategies, platform policies, and investment decisions.

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