Do managers use earnings forecasts to fill a demand they perceive from analysts?

Orie Barron, Jian Cao, Xuguang Sheng, Maya Thevenot, Baohua Xin

Research output: Chapter in Book/Report/Conference proceedingChapter


This paper examines how the nature of the information possessed by individual analysts influences managers’ decisions to issue forecasts and the consequences of those decisions. Our analytical model yields the prediction that managers prefer to issue guidance when they perceive their private information to be more precise, and analysts possess mostly common, imprecise information (i.e., there is high commonality and uncertainty). Based on an econometric model, we obtain theory-based analyst variables and our empirical evidence confirms our predictions. High commonality and uncertainty in analysts’ prior information are accompanied by increases in analysts’ forecast revisions and trading volume following guidance, consistent with greater analyst incentives to generate idiosyncratic information. Yet, management guidance increases only with the commonality contained in analysts’ pre-disclosure information, but not with the level of uncertainty. Indeed, the disclosure propensity among a subset of firms (those with less able managers, bad news, and infrequent forecasts) has an inverse relationship with analyst uncertainty due to its reflection on the low precision of management information. Our results are robust to a variety of alternative analyses, including the use of propensity-score matched pairs with similar disclosure environments but differing degrees of commonality and uncertainty among analysts. We also demonstrate that the use of forecast dispersion as an empirical proxy for analysts’ prior information may lead to erroneous inferences. Overall, we define and support improved measures of analyst information environment based on an econometric model and find that the commonality of information among analysts acts as a reliable forecast antecedent by informing managers about the amount of idiosyncratic information in the market.

Original languageEnglish (US)
Title of host publicationHandbook of Financial Econometrics, Mathematics, Statistics, and Machine Learning (In 4 Volumes)
PublisherWorld Scientific Publishing Co.
Number of pages49
ISBN (Electronic)9789811202391
ISBN (Print)9789811202384
StatePublished - Jan 1 2020

All Science Journal Classification (ASJC) codes

  • General Economics, Econometrics and Finance
  • General Business, Management and Accounting


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