The impact of accruals and lines of business on analysts’ earnings forecast superiority
AbstractIn 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. Copyright Springer Science+Business Media, LLC 2012
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Springer in its journal Review of Quantitative Finance and Accounting.
Volume (Year): 39 (2012)
Issue (Month): 3 (October)
Contact details of provider:
Web page: http://springerlink.metapress.com/link.asp?id=102990
Analysts’ quarterly earnings forecasts; Time-series quarterly earnings forecasts; Lines of business; Accruals; C22;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
You can help add them by filling out this form.
reading list or among the top items on IDEAS.Access and download statisticsgeneral information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn) or (Christopher F. Baum).
If references are entirely missing, you can add them using this form.