Non‐linear Equity Valuation: An Empirical Analysis
Bibliographic record
Abstract
LegacyCOMPUSTATData 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 inAtaullahet 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 theAtaullahet 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".