Adjusting Time for Uncertain Project Assessment
Bibliographic record
Abstract
This paper presents a novel methodology to include the risk when determining the net present value of an investment. More specifically, the risk of cash flows is priced by the delay in the payment of debts not at the initially agreed maturities, but at later dates. To do this, first we recall the classical methods which introduce a certain risk correcting parameter before determining the net present value of the project. The key idea of this new model is to transfer the risk to the time embedded in the expression of the discount function by using a suitable deformation of this parameter. In this way, the risk is measured by the delay in the initially agreed maturity when obtaining the corresponding cash flow. On the other hand, the way to include the risk in a project is based on an adaptation of the Krugman’s curve which describes the relationship between debt maturities and their respective expectations of being obtained. The empirical contribution is based on the use of real data of payment delays corresponding to Spanish companies in 2015. This procedure allows to fit the risk of an investment project from a more realistic perspective and so to determine more accurately its net present value.
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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 | 0.006 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 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".