Bibliographic record
Abstract
In corporate finance and valuation, we start off with the presumption that the riskfree rate is given and easy to obtain and focus the bulk of our attention on estimating the risk parameters of individuals firms and risk premiums. But is the riskfree rate that simple to obtain? Both academics and practitioners have long used government security rates as riskfree rates, though there have been differences on whether to use short term or long- term rates. In this paper, we not only provide a framework for deciding whether to use short or long term rates in analysis but also a roadmap for what to do when there is no government bond rate available or when there is default risk in the government bond. We look at common errors that creep into valuations as a consequence of getting the riskfree rate wrong and suggest a way in which we can preserve consistency in both valuation and capital budgeting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.039 | 0.173 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.023 |
| Scholarly communication | 0.011 | 0.057 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.007 | 0.020 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".