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Refining the Degree of Earnings Surprise: A Comparison of Statistical and Analysts' Forecasts
Author(s) -
Alexander John C.
Publication year - 1995
Publication title -
financial review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.621
H-Index - 47
eISSN - 1540-6288
pISSN - 0732-8516
DOI - 10.1111/j.1540-6288.1995.tb00842.x
Subject(s) - econometrics , earnings , autoregressive model , consensus forecast , heteroscedasticity , economics , autoregressive conditional heteroskedasticity , statistical model , statistical hypothesis testing , sample (material) , statistics , mathematics , finance , volatility (finance) , chemistry , chromatography
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.