Using forecast errors to explain revisions

Over the last four decades, numerous empirical studies have provided evidence that analyst forecasts are optimistically biased at the beginning of a fiscal year. However, analyst incentives to bias the forecast might change over the course of the year because the initial incentive to bias a forecast optimistically will be dominated by other incentives such as to be the most accurate forecaster at the end of the year. To improve forecast accuracy, analysts will therefore revise their forecasts for upcoming news, to account for the impact that news has on reported earnings and thus the initial forecast error. Moreover, analysts need to correct their forecasts, so as to compensate for the initial optimism bias. Since forecast revision is equal to the difference between two forecast errors, I argue that revisions are driven by the same determinants as forecast errors. In addition to the intuitive impact of news on revisions, I argue that a second major driver of revisions is the change in analyst incentives to systematically bias their earnings estimates. In this chapter, I draw on the literature of forecast error to derive a revision model that provides a new understanding of the drivers of forecast revisions.