Ratings of the Long–Term Projects: New Approach
Bibliographic record
Abstract
The paper continues create a new approach to rating methodology: in addition to two papers, which have considered the creditworthiness of the non–finance issuers (Brusov et al ., 2018c,d), we develop here a new approach to project rating. We work within investment models, created by authors. One of them describes the effectiveness of investment project from perspective of equity capital owners, while other model describes the effectiveness of investment project from perspective of equity capital and debt capital owners. The important features of current consideration as well as in previous studies are: 1) The adequate use of discounting of financial flows virtually not used in existing rating methodologies, 2) The incorporation of rating parameters (financial ratios), used in project rating, into considered modern investment models. Analyzing within these investment models with incorporated rating parameters the dependence of NPV on rating parameters (financial ratios) at different values of equity cost k 0 , at different values of credit rates k d as well as at different values of leverage level L we come to very important conclusion, that NPV in units of NOI ( NPV / NOI ) (as well as NPV in units of D (( NPV / D ) depends only on equity cost k 0 , on credit rates k d , on leverage level L as well as on one of the leverage ratios l j (on one of the coverage ratios i j ) and does not depend on equity value S , debt value D and NOI . This means that obtained results on the dependence of NPV (in units of NOI ) ( NPV / NOI ) on leverage ratios l j (as well as on the dependence of NPV (in units of D ) ( NPV / NOI ) on coverage ratios i j ) at different equity costs k 0 , at different credit rates k d , at different leverage levels L carry the universal character: these dependencies remain valid for investment projects with any equity value S , any debt value D and any NOI .
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".