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Management of Reported and Forecast EPS, Investor Responses, and Research Implications

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  • Foong Soon Cheong

    (New York University Shanghai, Shanghai 200122, China)

  • Jacob Thomas

    (Yale School of Management, Yale University, New Haven, Connecticut 06520)

Abstract

We document substantial management of reported and forecast earnings per share (EPS) for analyst-followed U.S. firms, with the extent of management increasing with share price. Managers smooth the volatility of reported EPS by using accruals to offset cash flow shocks. Smoother EPS is easier to forecast, resulting in smaller forecast errors. Managers also differentially guide forecasts to improve accuracy. Whereas unmanaged forecast errors are much larger for high-price firms, they are compressed to the point their magnitudes resemble those for low-price firms. Managers also guide analyst forecasts to generate patterns of forecast walkdowns that again vary with share price. Given the remarkable level of management implied by our results, we conduct additional robustness analyses. The strongest evidence is observed in stock price responses: investors recognize efforts to manage reported and forecast EPS and adjust accordingly. We highlight potential biases caused by researchers being unaware of managerial efforts and investor responses, and offer ways to mitigate those biases.

Suggested Citation

  • Foong Soon Cheong & Jacob Thomas, 2018. "Management of Reported and Forecast EPS, Investor Responses, and Research Implications," Management Science, INFORMS, vol. 64(9), pages 4277-4301, September.
  • Handle: RePEc:inm:ormnsc:v:64:y:2018:i:9:p:4277-4301
    DOI: 10.1287/mnsc.2017.2832
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    Cited by:

    1. David Veenman & Patrick Verwijmeren, 2022. "The Earnings Expectations Game and the Dispersion Anomaly," Management Science, INFORMS, vol. 68(4), pages 3129-3149, April.
    2. William Grieser & Charles J. Hadlock & Joshua R. Pierce, 2021. "Doing good when doing well: evidence on real earnings management," Review of Accounting Studies, Springer, vol. 26(3), pages 906-932, September.
    3. Florian Eugster & Alexander F. Wagner, 2021. "Earning investor trust: The role of past earnings management," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 48(1-2), pages 269-307, January.
    4. Jacob Thomas & Frank Zhang & Wei Zhu, 2021. "Dark Trading and Post-Earnings-Announcement Drift," Management Science, INFORMS, vol. 67(12), pages 7785-7811, December.
    5. Liu, Duan & Wang, Lili & Yan, Jing & Wan, Hong, 2023. "R&D manipulation and SEO pricing in the Chinese capital market: The information effect of inefficient investment," Research in International Business and Finance, Elsevier, vol. 65(C).
    6. Jake Thomas & Wentao Yao & Frank Zhang & Wei Zhu, 2022. "Meet, beat, and pollute," Review of Accounting Studies, Springer, vol. 27(3), pages 1038-1078, September.

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