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- W95190937 abstract "This paper develops a new approach to distinguishing clustering in analyst forecasts due to the causal influence of one analyst’s forecast on another from clustering due to a common response to correlated information. We apply this approach by studying how analysts who currently provide forecasts for a firm (“incumbent analysts) revise their forecasts in response to the first forecasts of analysts initiating coverage of the firm (“new” analysts). We find that, on average, incumbent analysts close the gap between new analysts’ forecasts and their own forecasts, consistent with clustering in forecasts. However, we conclude that this effect is not driven at all by the causal influence of new analysts’ forecasts, but rather by a common response to correlated information. Cross-sectional tests indicate that the effects of correlated information are stronger when the analysts involved are likely to be better-informed." @default.
- W95190937 created "2016-06-24" @default.
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- W95190937 date "2011-01-01" @default.
- W95190937 modified "2023-09-25" @default.
- W95190937 title "Causality or Correlation: Measuring the Impact of the Marginal Analyst" @default.
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- W95190937 doi "https://doi.org/10.2139/ssrn.1961158" @default.
- W95190937 hasPublicationYear "2011" @default.
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