Non‐linear Equity Valuation: An Empirical Analysis
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Bibliographic record
Abstract
Legacy COMPUSTAT Data pertaining to 226,165 firm‐year observations covering a 57 year period (1950–2006) across all industrial groups are used to empirically assess the likely form and magnitude of the biases that arise from linear equity valuation models. Linear equity valuation models dominate the empirical analysis of the literature but ignore a firm's growth and adaptation options, which, by default, are non‐linear in their determining variables. Given this, an orthogonal polynomial fitting procedure as summarized in A taullah et al . (2009), which does take account of the growth and adaptation options available to firms, is used to obtain a power series expansion for the relationship between equity prices and their determining variables. Our purpose is to assess whether the inclusion of the non‐linear terms associated with the growth and adaptation options available to firms can provide a more complete description of the relationship between equity prices and their determining variables when compared to the simple linear models that characterize the empirical research of this area of the literature. Our empirical analysis classifies firms into negative efficiency, low efficiency, and high efficiency levels and then for each efficiency level, estimates the parameters implied by the A taullah et al . (2009) orthogonal polynomial fitting procedure. Our results show that there is a very strong non‐linear relationship between equity value and its determining variables although the nature of the relationship varies according to the efficiency level considered.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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