When Adaptation is NOT the Option: Does the ERC Attenuate When Firms Are Less Likely to Use the Adaptation Option?
Bibliographic record
Abstract
This paper considers whether the earnings response coefficient (ERC) changes when firms are unlikely to adapt resources to other uses. Hayn (1995) hypothesizes and provides evidence consistent with losses having less information content than gains. Since losses are not expected to be persistent – a key determinate in the relationship between accounting earnings and returns – market participants discount those losses when formulating the expected value of the firm and instead value it based on the adaptation option, resulting in an attenuated ERC. However, when the likelihood of the adaptation option being used is low, I predict that the ERC will not attenuate since the market will still value the firm on earnings, even though they are negative. Using a book to market (BTM) ratio above one as my proxy for a reduced likelihood of adaptation, I am able to provide evidence consistent with my hypothesis. This research combines the prior literature on the adaptation option, the ERC attenuation, and the BTM ratio.
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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 | 0.008 | 0.068 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".