We examine whether the information conveyed in a relatively new analyst research output-capital expenditure (capex) forecasts-affects corporate investment efficiency. We find that firms with analyst capex forecasts exhibit higher investment efficiency. This effect is stronger when the forecasts are issued by analysts with higher ability or greater industry knowledge. Moreover, the effect of capex forecasts on investment efficiency varies with the signals they convey about future growth opportunities-positive-growth signals are more effective in reducing underinvestment, while negative-growth signals are more effective in reducing overinvestment. Cross-sectional tests suggest that these effects operate at least in part through both a financing channel and a monitoring channel. Taken together, our results suggest that analysts' capex forecasts convey useful information about firms' growth opportunities to managers and investors, which can facilitate efficient investment.
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 sizebased inferences about directional trading are quite misleading-producing
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