An Empirical Analysis of Analysts' Capital Expenditure Forecasts: Evidence from Corporate Investment Efficiency*
Bibliographic record
Abstract
ABSTRACT 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".