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Analysts move as a group toward consensus when those with superior information issue early earnings forecasts in a firm’s opaque information environment. The authors study the information environment of firms.

How Is This Research Useful to Practitioners?

Analysts issue forecasts regarding the investability of various firms on the basis of their research, which includes analysis of both public and private information about the firms. Analyst forecasts are an important piece of information for both portfolio managers and professional investors in making investment decisions. The influence of peers on analysts’ forecasts in an opaque information environment can materially affect the quality of their forecasts, thus affecting market efficiency.
Prior researchers have empirically proved that less informed analysts follow analysts with superior information toward consensus to preserve their reputations. The authors study the influence of firms’ information environment on analysts’ herding behavior.
They hypothesize that consensus-driven reputational herding behavior is positively related to the proportion of transient (i.e., short-term) institutional investors in a firm. A higher proportion of transient investors leads to an opaque information environment for a firm. In such cases, management-issued guidance is less valuable than the private information collated by analysts. Analysts with superior private information are followed by other analysts, thus creating a herd mentality regarding the forecasts about firms with less transparency in reporting fundamentals.
The authors’ second hypothesis states that the percentage of short-term institutional ownership is inversely proportional to the incidence of analyst revisions after management-issued guidance. After analyzing 8,315 firm-quarter observations, the authors find that the results do not support the hypothesized inverse relationship between transient ownership and analyst revisions.

How Did the Authors Conduct This Research?

The authors collect data on the institutional holdings of transient and long-term investors over 1998–2007. The sample data on herding behavior cover 2,408 firms, resulting in 8,045 firm-quarter observations. The sample data on analyst revisions cover 2,142 firms, resulting in 8,315 firm-quarter observations.
Data on management guidance are obtained from the First Call Company Issued Guidance database, and information around analyst revisions and consensus forecasts is derived from the First Call Analyst Estimate database. Using the estimated coefficient for transient ownership and the S-statistic (a measure of herding behavior), the authors show that analysts with inferior private information who cover transient-owned firms tend to move with the analysts having superior information, thus forming a consensus rather than making independent forecasts with their own analyses. They also show that analyst earnings revisions are meaningful and more likely when dedicated (i.e., long-term) institutional owners are associated with the firms.

Abstractor’s Viewpoint

Both public and private information regarding firms’ fundamentals lead to fair price discovery, which helps improve market efficiency. Very little attention has been given to the relationship between a firm’s information environment (opaque or otherwise) and analysts’ propensity to follow the herd. The authors’ research is an important addition to the literature on the effectiveness of analyst forecasts when a firm’s information environment is not transparent, which widens the information gap between the most informed and the least informed.
The authors also focus on the relationship between transient ownership and analyst revisions after the issuance of management guidance. They find that management guidance by a firm with dedicated ownership carries weight and probably results in analyst revisions to earnings forecasts. Dedicated ownership is a good proxy for effective monitoring, and market participants value information from better monitors of firm management.

About the Author(s)

Nitin Joshi