JOURNAL ARTICLE

Analysts' EPS-Decreasing Exclusions and Target Price Forecasts.

  • Published In: Management Science (INFORMS), 2026, v. 72, n. 4. P. 2895 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Larocque, Stephannie A.; Yu, Yong; Zhao, Wuyang 3 of 3

Abstract

This article investigates the relationship between individual financial analysts’ exclusions of positive nonrecurring earnings items—termed EPS-decreasing (EPSD) exclusions, which lower street earnings per share (EPS) forecasts relative to GAAP EPS forecasts—and the optimism embedded in their target price forecasts. Using a large sample of analyst forecasts from 2003 to 2020, the study finds that EPSD exclusions predominantly relate to positive one-time items already reported by firms and are associated with more optimistic target prices, partly because these exclusions enable analysts to project higher future earnings growth. The analysis further reveals that this association is stronger among analysts with greater strategic incentives to issue favorable valuations and those issuing buy recommendations, suggesting that EPSD exclusions serve as a tool for justifying optimistic target prices. These findings contribute to understanding how analysts’ definitions of earnings and their discretionary exclusions influence valuation forecasts, with implications for investors and regulators monitoring non-GAAP reporting and analyst behavior.

Additional Information

  • Source:Management Science (INFORMS). 2026/04, Vol. 72, Issue 4, p2895
  • Document Type:Article
  • Subject Area:Business and Management
  • Publication Date:2026
  • ISSN:0025-1909
  • DOI:10.1287/mnsc.2023.03627
  • Accession Number:192910487
  • Copyright Statement:Copyright of Management Science (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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