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Perceived, Projected, and True Investment Expertise: Not All Experts Provide Expert Recommendations

Authors :
Sameena Shah
Amit Shavit
Source :
DSAA
Publication Year :
2016
Publisher :
IEEE, 2016.

Abstract

Social networks enable knowledge sharing that inevitably begs the question of expertise analysis. Many online profiles claim expertise, but possessing true expertise is rare. We characterize expertise as projected expertise (claims of a person), perceived expertise (how the crowd perceives the individual) and true expertise (factual). StockTwits, an investor-focused microblogging platform, allows us to study all three aspects simultaneously. We analyze more than 18 million tweets spanning 1700 days. The large time scale allows us to also analyze expertise and its categories as they evolve over time, which is the first study of its kind on StockTwits. We propose a method to capture perceived expertise by how significantly a user's follower network grows and how often the user is brought up in conversations. We also quantify actual, market-based, true expertise based on the user's trade and investment recommendations. Finally we provide an analysis bringing out the differences between how users project themselves, how the crowd perceives them, and how they are actually performing on the market. Our results show that users who project themselves as experts are ones that talk the most and provide the least recommendation-to-tweet ratio (that is, most of their conversations are mundane). The recommendations from users who project novice expertise slightly outperform (≈5%) the overall stock market. On the other hand, the trade recommendations from self-proclaimed experts yield 80% less than those of intermediate traders. Interestingly, users who are perceived as experts by others, as measured by centrality measurements, resulted in net negative returns after a four year trading period. Our study also looks at the evolution of expertise, and begins to understand why and what makes users change the way they project their own expertise. For this topic, however, this paper introduces more questions than it answers, which will serve as the basis for future studies.

Details

Database :
OpenAIRE
Journal :
2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)
Accession number :
edsair.doi...........88287922d3bda204099490ae342c93e4