Aggregate Earnings: Time-series Properties and Forecasting Models.pdf

Aggregate Earnings: Time-series Properties and Forecasting Models false
By:Koren M. Jo
Published on 2015 by


In this study I examine the time series properties of quarterly aggregate earnings and aggregate analyst earnings forecasts. Using quarterly data from January 1988 to December 2012, I show that both quarterly aggregate earnings and analyst earnings forecasts follow a random walk process in contrast with the corresponding firm level's actual and forecasted earnings, which both follow a seasonal random walk process. The fact that aggregate earnings follows a random walk process suggests that researchers using aggregate earnings need to account for the serial correlation in aggregate earnings in their models. Moreover, the level of aggregate earnings and aggregate analyst forecasts are co-integrated even though aggregate analyst forecasts are biased. I further show that prior forecast errors are correlated with growth in aggregate GAAP earnings but not aggregate Street earnings. The exclusion of special items makes Street earnings smoother and easier to predict, thus making forecasts of Street earnings more efficient. Finally, random-walk-based forecasts outperform analyst forecasts in terms of accuracy when GAAP earnings are forecasted, but not when Street earnings are forecasted.

This Book was ranked at 22 by Google Books for keyword Forecasting.

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