Ratings of The Investment Projects of Arbitrary Durations: New Methodology
Bibliographic record
Abstract
In this paper we develop for the first time a new approach to ratings of the investment projects of arbitrary durations, which could be applied to investments of any area of economy and in particular to energy projects.The ratings of such energy projects, as "Turkish stream", "Nord stream-2", energy projects relating to clean, renewable and sustainable energy, as well as relating to pricing carbon emissions (McAleer et al., 2018a,b,c; 2019) could be done using developed here new rating methodologies. In our previous papers the new approach to the ratings of the long–term investment projects has been developed (Filatova et al., 2018). The important features of that consideration are as following: 1) The incorporation of rating parameters (financial "ratios"), used in project rating and playing a major role in it, into modern long–term investment models, 2) The adequate use of discounting of financial flows virtually not used in existing project rating methodologies. Here, for the first time, we incorporate the rating parameters (financial "ratios"), used in project rating, into modern investment models, describing the investment projects of arbitrary durations. This was much more difficult task then in case of the long–term investment projects, considered by us in previous papers. 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. New approach allows use the powerful instruments of modern theory of capital cost and capital structure (BFO theory) (Brusov et al., 2015, 2018) and modern investment models, created by the authors and well tested in the real economy to evaluate investment project performance, including energy projects.In our calculations we use Excel technique in two aspects: 1) we calculate WACC at different values of equity costs k0, different values of debt costs kd and different values of leverage level L=D/S, using the famous BFO formula; 2) we calculate the dependences of NPV on coverage ratios as well as leverage ratios at different values of equity costs k0, different values of debt costs kd and different values of leverage level L.
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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 | 0.002 | 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 | 0.000 | 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".