Equity pricing in the forest sector: evidence from North American stock markets
Bibliographic record
Abstract
Financial capital is very mobile and the failure to earn the cost of capital can result in capital flight and a considerably altered forest industry. This article seeks to assess the cost of equity capital for the forest sector from a modern finance perspective and test to what extent it has earned the cost of this capital over the period spanning from December 2003 to December 2008. To do so, using time series and cross-sectional methods, the capital asset pricing model and the Fama–French three-factor model were applied to the weekly returns of 45 publicly traded forest sector securities that are listed on North American stock exchanges. The time series results for both the capital asset pricing and Fama–French three-factor models yielded several negative pricing errors, suggesting ex post that many firms in the sector have failed to earn the cost of equity. Furthermore, cross-sectional results show that riskier firms tended to have lower returns. Such findings are unlikely to hold in the long run and could be one of the primary factors driving significant change in the forest sector in the future.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".