Crowds on Wall Street: Extracting Value from Collaborative Investing Platforms
Gang Wang
Tianyi Wang
Bolun Wang
Divya Sambasivan
Zengbin Zhang
Haitao Zheng
Ben Y. Zhao
Proceedings of 18th ACM conference on Computer-Supported Cooperative Work and Social Computing (CSCW 2015)
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Paper Abstract
In crowdsourced systems, it is often difficult to separate the highly
capable "experts" from the average worker. In this paper, we study the
problem of evaluating and identifying experts in the context of
SeekingAlpha and StockTwits, two crowdsourced investment services that are
encroaching 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 using their analysis significantly outperform broader markets.
Finally, we conduct a user survey that sheds light on users views of
SeekingAlpha content and stock manipulation.