Crowdsourced Earnings Expectations and the Salience of Sell-Side Forecast Bias

Schafhäutle, Sandra; Veenman, David

doi:10.2139/ssrn.3444144

Cited by 2 publications

(4 citation statements)

References 92 publications

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“…Similar results can be found for the crowd's earnings forecasts, even stronger when the contributing crowd is larger [2]. The existence of crowd reports also helps investors to mitigate a negative bias in institutional reports, improving the prediction of earnings surprises [21]. A recent study supports the growing relevance of crowd analyst, finding that bearish recommendations provide more accurate stock price prediction than recommendations in traditional analyst reports [3].…”

Section: Crowd Analystssupporting

confidence: 58%

Crowd Analysts vs. Institutional Analysts – A Comparative Study on Content and Opinion

Bankamp

Neuss

Muntermann

2021

Lecture Notes in Information Systems and Organisation

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The ongoing digital transformation shapes the world of information discovery and dissemination for investment decisions. Social investment platforms offer the possibility for non-professionals to publish financial analyst reports on company development and earnings forecast and give investment recommendations similar to those provided by traditional sell-side analysts. This phenomenon of "crowd analyst reports" has been found to provide an adequate alternative for non-professional investors. In this study, we examine the informational value of these crowd analyst reports regarding their timeliness in publishing and their originality as for content and opinion. Our findings suggest that crowd analysts strongly rely on previously published institutional reports. Therefore, crowd analysts do not pose a threat to institutional analysts at this time, however, they provide a more accessible information basis and improve decision-making for individual investors.

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Section: Crowd Analystssupporting

confidence: 58%

Crowd Analysts vs. Institutional Analysts – A Comparative Study on Content and Opinion

Bankamp

Neuss

Muntermann

2021

Lecture Notes in Information Systems and Organisation

View full text Add to dashboard Cite

show abstract

“…large firms in the initial years (Schafhäutle and Veenman [2021]), the sample firms are larger than those in related research (e.g., Green et al [2014]).…”

Section: Descriptive Statisticsmentioning

confidence: 98%

“…To identify private communication, I propose a novel measure motivated by the observation that analysts' and "other" forecasters' earnings forecast revisions diverge around quarterly earnings releases (Schafhäutle and Veenman [2021]). While analysts systematically lower their short-term earnings forecasts during the days of firms' earnings announcements, other forecasters do not revise estimates as strongly downwards.…”

Section: Measuring Firms' Private Disclosure Of Earnings Warningsmentioning

confidence: 99%

“…To hold constant the extent of analysts' forecast revisions due to public information, I benchmark analysts' forecast revisions to a control group of benchmark forecasters. The empirical strategy is motivated by Schafhäutle and Veenman [2021], who find that analysts strongly revise quarter q earnings forecasts around the days of quarter q − 1 earnings announcements, which is when manager-analyst private meetings peak (Soltes [2014]). The combined factors that analysts update earnings forecasts for quarter q at quarter q − 1 using public (e.g., 10-K, 10-Q, management forecasts) and private (e.g., private meetings with managers) information and benchmark forecasters only observe publicly available information lies at the heart of the empirical strategy to estimate managers' private information flows.…”

Section: Figure 1 Setting and Timelinementioning

confidence: 99%

See 1 more Smart Citation

Shareholder Litigation Risk and Managers’ Private Disclosure of Earnings Warnings

Schafhäutle

2021

SSRN Journal

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This paper examines managers' use of a private disclosure channel to provide early earnings warnings and therefore reduce bad news shocks around public announcements. I ask whether firms are more likely to provide private earnings warnings to analysts when expected shareholder litigation risk increases. To identify private communication, I measure variation in firms' propensity to privately disclose earnings warnings using the difference between analysts' and a selected group of benchmark forecasters' revisions of short-term earnings forecasts around earnings announcements. Using plausibly exogenous variation in expected shareholder litigation risk based on judge ideology, I find that managers are more likely to leak bad news privately when shareholder litigation risk increases. This effect is concentrated among large firms that are more likely the target of shareholder lawsuits. I conclude that firms react to the legal system by disclosing private earnings warnings to ensure stock prices incorporate this information in a timely manner.

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Crowdsourced Earnings Expectations and the Salience of Sell-Side Forecast Bias

Cited by 2 publications

References 92 publications

Crowd Analysts vs. Institutional Analysts – A Comparative Study on Content and Opinion

Crowd Analysts vs. Institutional Analysts – A Comparative Study on Content and Opinion

Shareholder Litigation Risk and Managers’ Private Disclosure of Earnings Warnings

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