The Cross-Section of Hurdle Rates for Capital Budgeting: An Empirical Analysis of Survey Data
Bibliographic record
Abstract
Whereas Poterba and Summers (1995) find that firms use hurdle rates that are unrelated to their CAPM betas, Graham and Harvey (2001) find that 74% of their survey firms use the CAPM for capital budgeting.We provide an explanation for these two apparently contradictory conclusions.We find that firms behave as though they add a hurdle premium to their CAPM based cost of capital.Following McDonald and Siegel (1986), we argue that the hurdle premium depends on the value of the option to defer investments.While CAPM explains only 10% of the cross-sectional variation in hurdle rates across firms, variables that proxy for the benefits from the option to wait for potentially better investment opportunities explain 35%.Estimates of our hurdle premium model parameters imply an equity premium of 3.8% per year, a figure that is essentially the same as that reported in the survey by Graham and Harvey (2005).Consistent with our model, growth firms use a higher hurdle rate when compared to value firms, even though they have a lower cost of capital.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".