The use of observed transaction sizes to differentiate between “small” and “large” investor trading patterns is widespread. A significant concern in such studies is spurious effects attributable to misclassification of transactions, particularly those originating from large investors. Such effects can arise unintentionally, strategically, or endogenously. We examine comprehensive records of a sample of institutional investors (i.e., “large” traders), including their order sizes and overall position changes, to assess the degree to which such misclassifications give rise to spurious inferences about “small” and “large” investor trading activities. Our analysis shows that these institutions are heavily involved in small transaction activity. It also shows that they increase their order sizes substantially in announcement periods relative to nonannouncement periods, presumably as an endogenous response to earnings news. In the immediate earnings announcement period, transaction size‐based inferences about directional trading are quite misleading—producing spurious “small trader” effects and, more surprisingly, erroneous inferences about “large trader” activity.

In September 2009, Thomson Reuters (TR) discontinued its practice of relying on analysts to determine the treatment of unexpected charges and gains in favor of their immediate exclusion from GAAP earnings. Adopting a difference‐in‐differences approach, we show that this plausibly exogenous change in TR's methodology resulted in street earnings that are more predictive of future performance; and timelier, more accurate, and less dispersed analyst forecasts of future earnings, consistent with TR enhancing the properties of street earnings and analyst forecasts. Finally, using path analysis we show that a significant portion of TR's effect on price discovery is through its effect on analysts; and that the change in TR's treatment of unexpected items increased (decreased) the relative influence of TR (analysts) on the pricing of street earnings. We conclude that forecast data providers like TR are more than a conduit of information from analysts to investors.