HOW DO CFOs MAKE CAPITAL BUDGETING AND CAPITAL STRUCTURE DECISIONS?
Bibliographic record
Abstract
This paper summarizes the findings of the authors' recent survey of 392 CFOs about the current practice of corporate finance, with main focus on the areas of capital budgeting and capital structure. The findings of the survey are predictable in some respects but surprising in others. For example, although the discounted cash flow method taught in our business schools is much more widely used as a project evaluation method than it was ten or 20 years ago, the popularity of the payback method continues despite shortcomings that have been pointed out for years. In setting capital structure policy, CFOs appear to place less emphasis on formal leverage targets that reflect the trade‐off between the costs and benefits of debt than on “informal” criteria such as credit ratings and financial flexibility. And despite the efforts of academics to demonstrate that EPS dilution per se should be irrelevant to stock valuation, avoiding dilution of EPS was the most cited reason for companies reluctance to issue equity. But despite such apparent contradictions between theory and practice, finance theory does seem to be gaining ground. For example, large companies were much more likely than their smaller counterparts to use DCF and NPV techniques, while small firms still tended to rely heavily on the payback criterion. And a majority of the CFOs of the large companies said they had “strict” or “somewhat strict” target debt ratios, whereas only a third of small firms claimed to have such targets. What does the future hold? On the one hand, the authors suggest that we are likely to see greater corporate acceptance of certain aspects of financial theory, including the use of real options techniques for evaluating corporate investments. But we are also likely to see further modifications and refinements of the theory, particularly with respect to smaller companies that have limited access to capital markets.
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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 | 0.018 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".