Do management earnings forecasts fully reflect information in past earnings changes?

Guojin Gong, Yue Li, Ling Zhou

Research output: Contribution to journalArticle

Abstract

Purpose: It has been widely documented that investors and analysts underreact to information in past earnings changes, a fundamental performance indicator. The purpose of this paper is to examine whether managers’ voluntary disclosure efficiently incorporates information in past earnings changes, whether analysts recognize and fully anticipate the potential inefficiency in management forecasts and whether managers’ potential forecasting inefficiency entirely results from intentional disclosure strategies or at least partly reflects managers’ unintentional information processing biases. Design/methodology/approach: Archival data were used to empirically test the relation between management earnings forecast errors and past earnings changes. Findings: Results show that managers underreact to past earnings changes when projecting future earnings and analysts recognize, but fail to fully anticipate, the predictable bias associated with past earnings changes in management forecasts. Moreover, analysts appear to underreact more to past earnings changes when management forecasts exhibit greater underestimation of earnings change persistence. Further analyses suggest that the underestimation of earnings change persistence is at least partly attributable to managers’ unintentional information processing bias. Originality/value: This study contributes to the voluntary disclosure literature by demonstrating the limitation in the informational value of management forecasts. The findings indicate that the effectiveness of voluntary disclosure in mitigating market mispricing is inherently limited by the inefficiency in management forecasts. This study can help market participants to better use management forecasts to form more accurate earnings expectations. Moreover, our evidence suggests a managerial information processing bias with respect to past earnings changes, which may affect managers' operational, investment or financing decisions.

Original languageEnglish (US)
Pages (from-to)373-406
Number of pages34
JournalInternational Journal of Accounting and Information Management
Volume27
Issue number3
DOIs
Publication statusPublished - Aug 5 2019

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All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Accounting
  • Economics, Econometrics and Finance(all)

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