Market Multiples and the Valuation of Cyclical Companies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Market multiples are more often used than studied. Equity analysts, investment bankers and other practitioners widely use market multiples to estimate the value of companies. Nevertheless, literature about multiples is not as rich as the wide use of these valuation tools would suggest. This paper, focusing on European listed companies, investigates how multiples can be used in the valuation of cyclical companies, a much less investigated research topic. We test the accuracy of multiples to understand whether their performance in valuing cyclical companies is better, worse or equal to the performance found in prior studies, where both cyclical and non cyclical companies are analyzed without distinguishing between them. We also attempt to verify whether the way in which multiples are calculated significantly affects the accuracy of estimation. Our aim is to develop a valuation approach consistent with valuation theory and helpful in everyday practice.
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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 | 0.006 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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