Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media

Chen, Hailiang; De, Prabuddha; Hu, Yu Jeffrey; Hwang, Byoung-Hyoun

doi:10.1093/rfs/hhu001

Cited by 958 publications

(567 citation statements)

References 47 publications

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“…They also find an economically small effect in the aggregated messages' tone's ability to predict future returns. In contrast, Chen et al (2014) find that aggregated tone from articles on Seeking Alpha helps predict earning surprises and subsequent stock returns, with economically significant magnitudes. A factor which reconciles their findings may be the underlying sophistication of the users within their respective samples.…”

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confidence: 63%

The Search for Benchmarks: When Do Crowds Provide Wisdom?

Lee

Wang³

2014

SSRN Journal

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We compare the performance of a comprehensive set of alternative peer identification schemes used in economic benchmarking. Our results show the peer firms identified from aggregation of informed agents' revealed choices in Lee, Ma, and Wang (2014) perform best, followed by peers with the highest overlap in analyst coverage, in explaining cross-sectional variations in base firms' out-of-sample: (a) stock returns, (b) valuation multiples, (c) growth rates, (d) R&D expenditures, (e) leverage, and (f) profitability ratios. Conversely, peers firms identified by Google and Yahoo Finance, as well as product market competitors gleaned from 10-K disclosures, turned in consistently worse performances. We contextualize these results in a simple model that predicts when information aggregation across heterogeneously informed individuals is likely to lead to improvements in dealing with the problem of economic benchmarking. JEL: D83, G11Keywords: peer firm, benchmarking, EDGAR search traffic, co-search, analyst coverage, industry classification, wisdom of crowds * The authors can be contacted at clee8@stanford.edu, paulma@umn.edu, and charles.cy.wang@hbs.edu. We thank Boris Groysberg, Paul Healy, Ryan Buell, Kai Du, Akash Chattopadhyay, Andrew Jing Liu, Daniel Malter, Tatiana Sandino, Pian Shu, Martin Szydlowski, Akhmed Umyarov, and Aaron Yoon for helpful comments and suggestions. We are very grateful to Scott Bauguess at the Securities and Exchange Commission for assistance with the EDGAR search traffic data. We also thank Kyle Thomas for excellent research assistance. All errors remain our own.

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confidence: 63%

The Search for Benchmarks: When Do Crowds Provide Wisdom?

Lee

Wang³

2014

SSRN Journal

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“…Their model, when applied to empirical data, shows that the price impact related to the news is initially strengthened by the inf luence, yet the convergence to the true value could be slowed later on. Chen et al [2014] incorporated the textual sentiments from a popular crowd-sourced financial website, Seeking Alpha, in their research and found that a strong link exists between investors' opinions and security prices. Their regression results show that stock reviews on Seeking Alpha have remarkable predictive power for future equity returns and earnings surprises.…”

Section: Literature Reviewmentioning

confidence: 99%

Tweet Sentiments and Crowd-Sourced EarningsEstimates as Valuable Sources of Information aroundEarnings Releases

Liew¹,

Guo

Zhang

2016

JAI

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“…As far as the finance literature is concerned, the pioneering work of Tetlock (2007) Other studies report evidence of predictive power of stock message boards and major financial columns on volatility, returns and volume (Antweiler and Frank (2004) , Chen, De, Hu, and Hwang (2013)). The related literature also studies the effect of returns on media content Garcıa (2012), the effect of media content on returns during recessions and expansions Garcia (2013)), while a high level of similarity in firm-specific news is found to provoke higher trading aggressiveness of individual investors (Tetlock (2011)).…”

Section: Textual Analysis Literaturementioning

confidence: 99%

The Pessimism Factor: SEC EDGAR Form 10-K Textual Analysis and Stock Returns

Chouliaras

2015

SSRN Journal

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I perform textual analysis on 20,000 annual SEC 10-K Forms, for NYSE, NASDAQ and AMEX stocks, from 1992 until 2015. The textual analysis negative (pessimism) percentage per se, as used in the previous literature, is not a significant determinant of future stock returns. But, monthly portfolios based on the product of annual pessimism change and the previous period returns generate returns in excess of previous winners/losers. Nine months after the filing, the difference is higher than 5%, while it surpasses 7% twelve months after the filing. Negative (positive) previous returns along with positive pessimism changes lead to positive (negative) returns.JEL classification: G10, G14.

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Wisdom of Crowds: The Value of Stock Opinions Transmitted Through Social Media

Cited by 958 publications

References 47 publications

The Search for Benchmarks: When Do Crowds Provide Wisdom?

The Search for Benchmarks: When Do Crowds Provide Wisdom?

Tweet Sentiments and Crowd-Sourced EarningsEstimates as Valuable Sources of Information aroundEarnings Releases

The Pessimism Factor: SEC EDGAR Form 10-K Textual Analysis and Stock Returns

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