The impact of accruals and lines of business on analysts’ earnings forecast superiority

The impact of accruals and lines of business on analysts’ earnings forecast superiority In this paper, we examine the linkage between analyst advantage (AA) (compared to the seasonal random walk model) in the prediction of quarterly earnings-per-share (EPS) and a broad set of economic determinants. Specifically, we employ a pooled cross-sectional time-series regression model where AA is linked to a set of firm-specific economic determinants that have been employed in extant work (e.g., Brown et al. in J Account Res 22:49–67, 1987; Kross et al. in Account Rev 65:461–476, 1990). We refine this set of independent variables by including a new variable (RATIODEV) based upon Sloan (Account Rev 71(3):289–315, 1996) who documents that differential levels of accruals impact future earnings performance. This variable is particularly salient in explaining AA since analysts may be in a position to identify the permanent component of accruals via fundamental financial analysis. Additionally, we refine the measurement of lines of business—consistent with the reporting requirements of SFAS No. 131 relative to extant work that operationalized proxies for this variable based upon SFAS No. 14. Parameters for these aforementioned variables are significantly positively related to AA, consistent with theory. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Review of Quantitative Finance and Accounting Springer Journals

The impact of accruals and lines of business on analysts’ earnings forecast superiority

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Publisher
Springer Journals
Copyright
Copyright © 2011 by Springer Science+Business Media, LLC
Subject
Finance; Corporate Finance; Accounting/Auditing; Econometrics; Operation Research/Decision Theory
ISSN
0924-865X
eISSN
1573-7179
D.O.I.
10.1007/s11156-011-0254-z
Publisher site
See Article on Publisher Site

Abstract

In this paper, we examine the linkage between analyst advantage (AA) (compared to the seasonal random walk model) in the prediction of quarterly earnings-per-share (EPS) and a broad set of economic determinants. Specifically, we employ a pooled cross-sectional time-series regression model where AA is linked to a set of firm-specific economic determinants that have been employed in extant work (e.g., Brown et al. in J Account Res 22:49–67, 1987; Kross et al. in Account Rev 65:461–476, 1990). We refine this set of independent variables by including a new variable (RATIODEV) based upon Sloan (Account Rev 71(3):289–315, 1996) who documents that differential levels of accruals impact future earnings performance. This variable is particularly salient in explaining AA since analysts may be in a position to identify the permanent component of accruals via fundamental financial analysis. Additionally, we refine the measurement of lines of business—consistent with the reporting requirements of SFAS No. 131 relative to extant work that operationalized proxies for this variable based upon SFAS No. 14. Parameters for these aforementioned variables are significantly positively related to AA, consistent with theory.

Journal

Review of Quantitative Finance and AccountingSpringer Journals

Published: Oct 5, 2011

References

  • The impact of SFAS No. 131 on information and monitoring
    Berger, P; Hann, R
  • Further evidence of the time-series properties of accounting income
    Brooks, LD; Buckmaster, DA
  • Earnings based and accrual-based anomalies: one effect or two
    Collins, DW; Hribar, P
  • The persistence and pricing of the cash component of earnings
    Dechow, P; Sloan, R; Richardson, S

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