Determination of Market Values and Risk Premia of Multi-national Enterprises and Its Application to Transfer-pricing
Bibliographic record
Abstract
Valuing a multi-national enterprise (MNE) using the discounted cash flow method (DCF) requires the joint determination of the market value of its equity (MVE) together with the equity risk premium (ERP) the firm should earn, since the latter is part of the discount rate used in the calculation of the MVE. This paper presents a theoretical derivation of how MVE and ERP can be calculated simultaneously under fairly general conditions and an application example. Besides firm data on free cash flow to equity the only external data needed are the risk-free rate of interest and a parameter indicating the required market risk premium per return volatility. The method presented allows for consistent valuation in particular of those firms that are not publicly listed and where ownership shares are not publicly traded. It also allows comparing the cash flows themselves to market returns on equally risky assets. This latter possibility is useful in transfer pricing, where the profit levels of dependent subsidiaries of MNEs are frequently under investigation.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".