The Implied Cost of Capital: An Empirical Assessment in the Tunisian Context
Bibliographic record
Abstract
This research is a feedback to Wang (2015) suggesting that realized returns should be used in conjunction with ICCs to make more robust inferences about expected returns. We examine the validity of six firm-specific ICCs along with a synthetic one, in the Tunisian context, according to their feasibility and their correlation with realized return. The examined estimators are calculated according to three types of earnings forecasts: smoothing, random walk and cross-section. These estimators represent three main valuation approaches: Present Value of Expected Dividend (PVED), Residual Income Valuation Model (RIV) and Abnormal Earnings Growth (AEG). Our results confirm the assertions of Gerakos and Gramacy (2013) on random walk forecasts’ good performance as well as those of Li and Mohanram (2014) on the poor quality of Hou et al. (2012)’s cross-section forecasts. Furthermore, dividend seems best reflecting Tunisian stock market expectations concerning future revenues which would be generated by the valuated asset. These findings bring into question the relevance of new accounting valuation approaches which are anchored rather on equity book value (RIV) and on earnings forecasts (AEG).
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".