Herding by Top Analysts
SSRN Electronic Journal
2015
- 1Citations
- 2,019Usage
- 2Captures
- 1Mentions
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
It is conventionally perceived in the literature that weak analysts are likely to under-weight their private information and strategically bias their announcements in the direction of the public beliefs to avoid scenarios where their private information turns out to be wrong, whereas strong analysts tend to adopt an opposite strategy of over-weighting their private information and shifting their announcements away from the public beliefs in an attempt to stand out from the crowd. Analyzing a reporting game between two financial analysts, who are compensated based on their relative forecast accuracy, we demonstrate that it could be the other way around. An investigation of the equilibrium in our game suggests that, contrary to the common perception, analysts who benefit from information advantage may strategically choose to understate their exclusive private information and bias their announcements toward the public beliefs, while exhibiting the opposite behavior of overstating their private information when they estimate that their peers are likely to be equally informed.
Bibliographic Details
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