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Consensus Forecasts of Corporate Earnings: Analysts' Forecasts and Time Series Methods

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Author Info

  • Robert Conroy

    (School of Business Administration, University of North Carolina, Chapel Hill, North Carolina 27514)

  • Robert Harris

    (School of Business Administration, University of North Carolina, Chapel Hill, North Carolina 27514)

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    Abstract

    An alternative to using a single forecasting method is to average the forecasts made by various methods. In this paper we examine empirically combinations of financial analysts' forecasts and forecasts from time series methods in order to predict corporate earnings per share. We conclude that, on average, the primary forecasting advantages of analysts over time series methods based on annual data appear to occur over short forecast horizons (less than a year). Neither analysts nor other time series methods substantially outperform a random walk prediction of no change when forecasts are made near the beginning of the fiscal year. For predictions in the first half of the fiscal year, there is evidence of forecasting benefits from combining time series and analysts' forecasts, especially if there are few analysts' forecasts.

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    File URL: http://dx.doi.org/10.1287/mnsc.33.6.725
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 33 (1987)
    Issue (Month): 6 (June)
    Pages: 725-738

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    Handle: RePEc:inm:ormnsc:v:33:y:1987:i:6:p:725-738

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    Related research

    Keywords: forecasting; financial analysts; consensus; earnings;

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    Cited by:
    1. Martin Wallmeier, 2005. "Analysts’ Earnings Forecasts for DAX100 Firms During the Stock Market Boom of the 1990s," Financial Markets and Portfolio Management, Springer, vol. 19(2), pages 131-151, August.
    2. Andersson, Patric, 2004. "How well do financial experts perform? A review of empirical research on performance of analysts, day-traders, forecasters, fund managers, investors, and stockbrokers," Working Paper Series in Business Administration 2004:9, Stockholm School of Economics.
    3. Elkin Castaño Vélez & Luis Fernando Melo Velandia, 2000. "Metodos de combinacion de pronosticos: una aplicacion a la inflacion," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 52, pages 113-165, Enero Jun.
    4. Andersson, Patric & Edman, Jan & Ekman, Mattias, 2005. "Predicting the World Cup 2002 in soccer: Performance and confidence of experts and non-experts," International Journal of Forecasting, Elsevier, vol. 21(3), pages 565-576.
    5. Webby, Richard & O'Connor, Marcus, 1996. "Judgemental and statistical time series forecasting: a review of the literature," International Journal of Forecasting, Elsevier, vol. 12(1), pages 91-118, March.
    6. Aigbe Akhigbe & Ronald Kudla & Jeff Madura, 2005. "Why are some corporate earnings restatements more damaging?," Applied Financial Economics, Taylor & Francis Journals, vol. 15(5), pages 327-336.
    7. Sandip Dhole & Sagarika Mishra & Ananda M Pal, . "Further Evidence on the Importance of Analysts’ Cash Flow Forecasts," Financial Econometics Series 2013_01, Deakin University, Faculty of Business and Law, School of Accounting, Economics and Finance.

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