Value and Misinformation in Collaborative Investing Platforms
Tianyi Wang
Gang Wang
Bolun Wang
Divya Sambasivan
Zengbin Zhang
Xing Li
Haitao Zheng
Ben Y. Zhao
ACM Transactions on the Web (TWEB), Vol. 11, No. 2, May 2017
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Paper Abstract
It is often difficult to separate the highly capable "experts" from the average worker in
crowdsourced systems. This is especially true for challenge application domains that
require extensive domain knowledge. The problem of stock analysis is one such domain,
where even the highly paid, well-educated domain experts are prone to make mistakes. As an
extremely challenging problem space, the "wisdom of the crowds" property that many
crowdsourced applications rely on may not hold. In this article, we study the problem of
evaluating and identifying experts in the context of SeekingAlpha and StockTwits, two
crowdsourced investment services that have recently begun to encroach on a space dominated
for decades by large investment banks. We seek to understand the quality and impact of
content on collaborative investment platforms, by empirically analyzing complete datasets
of SeekingAlpha articles (9 years) and StockTwits messages (4 years). We develop sentiment
analysis tools and correlate contributed content to the historical performance of relevant
stocks. While SeekingAlpha articles and StockTwits messages provide minimal correlation
to stock performance in aggregate, a subset of experts contribute more valuable
(predictive) content. We show that these authors can be easily identified by user
interactions, and investments based on their analysis significantly outperform broader
markets. This effectively shows that even in challenging application domains, there is a
secondary or indirect wisdom of the crowds. Finally, we conduct a user survey that sheds
light on users' views of SeekingAlpha content and stock manipulation. We also devote
efforts to identify potential manipulation of stocks by detecting authors controlling
multiple identities.