Growth and value hybrid valuation model based on mean reversion
Why this work is in the frame
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Bibliographic record
Abstract
This article proposes a novel valuation model, growth and value hybrid model, to estimate the stock price. This proposed model combines the essence of the asset-based approach, the income-based approach, and the principle of mean reversion to develop the theoretical closed-form formula consisting of three coefficients: value coefficient, value support coefficient and growth coefficient. Regression analysis is employed to fit market data to determine these coefficients. Moreover, this study proposes the double sorting method to build the quantile regression models of the formula to estimate the stock price at a specific quantile. The results show that the predictive capability of the hybrid valuation model is superior to the model without using value support coefficient, which supports the assumption that the PBR is not associated with the ROE when the ROE is less than a threshold. In different time periods of the stock market, no significant difference exists on the value support coefficient. However, the variations of the value coefficient and the growth coefficient are significant.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 it