Implied Cost of Equity Capital in Earnings-Based Valuation: International Evidence
Bibliographic record
Abstract
Assuming the clean surplus relation, the Edwards-Bell-Ohlson residual income valuation (RIV) model expresses market value of equity as the sum of the book value of equity and the expected discounted future residual incomes. Without assuming the clean surplus relation, Ohlson and Juettner-Nauroth (2000) articulate the role of forward earnings per share in valuation. We compare the implied costs of equity capital from these two approaches to earnings-based valuation within seven developed countries. We hypothesise superior performance from the RIV model in countries where the clean surplus relation holds well. First, we provide preliminary international evidence on the frequency and magnitude of the clean surplus deviations. Consistent with our hypothesis, we document superior reliability of the implied cost of equity capital derived from the RIV model when clean surplus adequately describes the firms' financial reporting. That is, the implied cost of equity capital derived from Ohlson and Juettner-Nauroth (2000) is relatively more reliable in countries where the clean surplus deviations are common. Our analyses suggest that the proper choice of earnings-based valuation model may depend on analysts' interpretation of their financial reporting environment.
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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 | 0.011 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| 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".