Equity Valuation Employing the Ideal versus Ad Hoc Terminal Value Expressions
Why this work is in the frame
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Bibliographic record
Abstract
Recently, Penman and Sougiannis (1998) and Francis, Olsson and Oswald (1999) compared the bias and accuracy of the dividend discount model (DDM), discounted cash flow model (DCF), and Edwards-Bell-Ohlson residual income model (RIM) in explaining the relation between value estimates and observed stock prices. Both studies report that, with non price-based terminal values, RIM outperforms DCF and DDM. Our primary research objective is to explore whether, over a five-year valuation horizon, DDM, DCF and RIM are empirically equivalent when Penman's (1998) theoretically terminal value expressions are employed in each model. Using Value Line terminal stock price forecasts at the horizon to proxy for such values, we find empirical support for the prediction of equivalence between these three price-based valuation models. Our secondary research objective is to demonstrate that, within each class of the DCF and RIM valuation models, the model that employs Value Line forecasted price in the terminal value expression will generate the lowest pricing errors, compared to models that employ non price-based terminal value under an arbitrary growth assumption. Results indicate that, for both DCF and RIM, price-based valuation models outperform the corresponding non price-based models by a wide margin. We also revisit the issue of the apparent superiority of RIM, and find that this result does not hold in a level playing field where an approximation of ideal terminal values is employed. In fact, the price-based RIM model is marginally outperformed by the price-based DCF and DDM models, in terms of pricing errors as well as its ability to explain current market price.
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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 | 0.020 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it