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

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  • Kenneth Lorek

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  • Donald Pagach

    ()

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    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. Copyright Springer Science+Business Media, LLC 2012

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    Bibliographic Info

    Article provided by Springer in its journal Review of Quantitative Finance and Accounting.

    Volume (Year): 39 (2012)
    Issue (Month): 3 (October)
    Pages: 293-308

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    Handle: RePEc:kap:rqfnac:v:39:y:2012:i:3:p:293-308

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    Web page: http://springerlink.metapress.com/link.asp?id=102990

    Related research

    Keywords: Analysts’ quarterly earnings forecasts; Time-series quarterly earnings forecasts; Lines of business; Accruals; C22;

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    References

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    1. Patricia M. Dechow & Scott A. Richardson & Richard G. Sloan, 2008. "The Persistence and Pricing of the Cash Component of Earnings," Journal of Accounting Research, Wiley Blackwell, vol. 46(3), pages 537-566, 06.
    2. Philip G. Berger & Rebecca Hann, 2003. "The Impact of SFAS No. 131 on Information and Monitoring," Journal of Accounting Research, Wiley Blackwell, vol. 41(2), pages 163-223, 05.
    3. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-38, May.
    4. Bernard, Victor L. & Thomas, Jacob K., 1990. "Evidence that stock prices do not fully reflect the implications of current earnings for future earnings," Journal of Accounting and Economics, Elsevier, vol. 13(4), pages 305-340, December.
    5. Stickel, Scott E, 1992. " Reputation and Performance among Security Analysts," Journal of Finance, American Finance Association, vol. 47(5), pages 1811-36, December.
    6. Bhushan, Ravi, 1989. "Firm characteristics and analyst following," Journal of Accounting and Economics, Elsevier, vol. 11(2-3), pages 255-274, July.
    7. Ball, Ray & Bartov, Eli, 1996. "How naive is the stock market's use of earnings information?," Journal of Accounting and Economics, Elsevier, vol. 21(3), pages 319-337, June.
    8. Joshua Livnat & Richard R. Mendenhall, 2006. "Comparing the Post-Earnings Announcement Drift for Surprises Calculated from Analyst and Time Series Forecasts," Journal of Accounting Research, Wiley Blackwell, vol. 44(1), pages 177-205, 03.
    9. Harrison Hong & Jeffrey D. Kubik, 2003. "Analyzing the Analysts: Career Concerns and Biased Earnings Forecasts," Journal of Finance, American Finance Association, vol. 58(1), pages 313-351, 02.
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