Winning by Following the Winners: Mining the Behaviour of Stock Market Experts in Social Media

Liao, Wenhui; Shah, Sameena; Makrehchi, Masoud

doi:10.1007/978-3-319-05579-4_13

Cited by 7 publications

(3 citation statements)

References 11 publications

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“…Other work [31] invests on all possible stocks in the market. Prior works [5,28] use small datasets from Twitter/StockTwits to quantify the noisiness in overall sentiment, and to motivate the need for contributions from experts. This is consistent with our results that show the best performance is achieved from identifying and relying on top experts.…”

Section: Sentiment-based Investing and Analysismentioning

confidence: 99%

Crowds on Wall Street

Wang

et al. 2015

Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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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.

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Section: Sentiment-based Investing and Analysismentioning

confidence: 99%

Crowds on Wall Street

Wang

et al. 2015

Proceedings of the 18th ACM Conference on Computer Supported Cooperative Work &Amp; Social Computing

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“…Other work invests in all possible stocks in the market [Makrehchi et al 2013]. Prior works [Bar-Haim et al 2011;Liao et al 2014] use small datasets from Twitter/StockTwits to quantify the noisiness in overall sentiment and to motivate the need for contributions from experts. This is consistent with our results that show the best performance is achieved from identifying and relying on top experts.…”

Section: Related Workmentioning

confidence: 99%

Value and Misinformation in Collaborative Investing Platforms

Wang

et al. 2017

ACM Trans. Web

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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.

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“…Other work [23] invests on all possible stocks in the market. Prior works [4,20] use small datasets from Twitter/StockTwits to quantify the high level of noise in overall sentiment, and to motivate the need for contributions from experts. This is consistent with our results that show the best performance is achieved from identifying and relying on top experts.…”

Section: Sentiment-based Investment Strategiesmentioning

confidence: 99%

Crowds on Wall Street: Extracting Value from Social Investing Platforms

Wang,

Wang

et al. 2014

Preprint

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For decades, the world of financial advisors has been dominated by large investment banks such as Goldman Sachs. In recent years, user-contributed investment services such as SeekingAlpha and StockTwits have grown to millions of users. In this paper, we seek to understand the quality and impact of content on social 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 authors contribute more valuable (predictive) content. We show that these authors can be identified via both empirical methods or 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. ID Question Tester 1 How many years of experience do you have in investing in the stock market? Author & User 2 How often do you trade stocks based on opinions and information gathered from reading SeekingAlpha articles? User 3 Do you think you can trust the authors on SeekingAlpha? User 4 How often have you seen articles on SeekingAlpha that looked like they were heavily biased, written with the intent to manipulate a particular stock (to move its price up or down)? Author & User 5 If and when you did see what looked like an article intent on stock manipulation, what is your general reaction? User 6 Do you think SeekingAlpha articles can impact the future movement of stocks they focus on? Author 7 If SeekingAlpha did not exist, where would you post your investment ideas? Author Table 3. Survey questions to SeekingAlpha authors and normal users.

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Winning by Following the Winners: Mining the Behaviour of Stock Market Experts in Social Media

Cited by 7 publications

References 11 publications

Crowds on Wall Street

Crowds on Wall Street

Value and Misinformation in Collaborative Investing Platforms

Crowds on Wall Street: Extracting Value from Social Investing Platforms

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