Evidence on the Effect of Multiple Corporate Restructurings on Analysts' Earnings Forecasts: Do Analysts Learn from Prior Restructuring Events?
36 Pages Posted: 26 Jun 2000
Date Written: April 2000
The primary objective of this study is to investigate the effect of prior and multiple restructuring charges on analysts? earnings forecasts. We investigate the effect of restructuring charges on analysts'forecasts by examining both forecast accuracy and dispersion. Chaney et al. (1999) provide evidence that analyst forecast accuracy is impaired by restructurings. However, they. find no empirical link between prior restructuring events and forecast accuracy. In contrast to the findings of Chaney et al., we predict that analysts will learn from prior restructuring charges. By "learn" we mean that current restructuring charges impair forecast accuracy to a lesser extent when prior restructuring charges are present. To document learning, we adapt the Chaney model and partition the sample to identify restructuring plans that have been fully implemented. Additionally, we control for the complexity of the prior charges.
Consistent with our prediction, we find that analysts are able to learn from prior restructuring events. Further tests suggest that this learning is only related to restructurings that have been fully implemented, that is, previous events that were announced more than two years prior to the current forecast. Additionally, we find that the relative magnitude of restructuring charges are associated with a decrease (increase) in forecast accuracy (dispersion) for up to two years after the announcement of the event. This result is consistent with the findings of Hanna (1999). Overall, our results are generally consistent with the conclusion that restructurings create uncertainty for analysts that lasts for at least two years subsequent to the announcement of the event and that analysts do in fact learn from the existence of prior events.
JEL Classification: M41, M44, G29, G34
Suggested Citation: Suggested Citation