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Refining the Degree of Earnings Surprise: A Comparison of Statistical and Analysts' Forecasts

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  • Alexander, John C, Jr

Abstract

This paper compares the relative predictive ability of several statistical models with analysts' forecasts. It is one of the first attempts to forecast quarterly earnings using an autoregressive conditional heteroskedasticity (ARCH) model. ARCH and autoregressive integrated moving average models are found to be superior statistical forecasting alternatives. The most accurate forecasts overall are provided by analysts. Analysts have both a contemporaneous and timing advantage over statistical models. When the sample is screened on those firms that have the largest structural change in the earnings process, the forecast accuracy of the best statistical models is similar to analysts' predictions. Copyright 1995 by MIT Press.

Suggested Citation

  • Alexander, John C, Jr, 1995. "Refining the Degree of Earnings Surprise: A Comparison of Statistical and Analysts' Forecasts," The Financial Review, Eastern Finance Association, vol. 30(3), pages 469-506, August.
    • Handle: RePEc:bla:finrev:v:30:y:1995:i:3:p:469-506
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    Cited by:

    1. Seifert, Matthias & Hadida, Allègre L., 2013. "On the relative importance of linear model and human judge(s) in combined forecasting," Organizational Behavior and Human Decision Processes, Elsevier, vol. 120(1), pages 24-36.
    2. Lawrence, Michael & Goodwin, Paul & O'Connor, Marcus & Onkal, Dilek, 2006. "Judgmental forecasting: A review of progress over the last 25 years," International Journal of Forecasting, Elsevier, vol. 22(3), pages 493-518.
    3. Darrat, Ali F & Zhong, Maosen, 2000. "On Testing the Random-Walk Hypothesis: A Model-Comparison Approach," The Financial Review, Eastern Finance Association, vol. 35(3), pages 105-124, August.

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