Real Earnings Management and the Properties of Analysts\u27 Forecasts

Prior literature generally finds analysts are able to identify and process complex financial information. However, research suggests that in certain settings, analysts struggle to fully incorporate into their forecasts all available information. We examine analysts\u27 forecast properties in the face of a specific type of complex financial information: real earnings management (REM). First, we investigate the relation between measures of REM and analysts\u27 forecast properties. We find REM measures are associated with greater forecast error and dispersion in the following year. However, REM measures, by definition, capture abnormal operating results, and thus include both firms engaging in manipulative REM as well as firms experiencing firm-specific economic shocks. Thus, we conduct cross-sectional tests of analysts\u27 forecasts for firms with and without incentives to manipulate earnings. We find that firms with low incentives to engage in earnings management (i.e., firms most likely experiencing firm-specific economic shocks) generate the strongest positive relation between REM measures and the following year\u27s analysts\u27 forecast properties, suggesting analysts more fully incorporate the earnings implications of firms with high incentives (i.e., firms most likely engaging in manipulative REM). Our results are consistent across numerous REM proxies and indicators of earnings management incentives