Resource Allocation Effects of Price Reactions to Disclosures*
Bibliographic record
Abstract
Abstract Capital market participants collectively may possess information about the valuation implications of a firm's change in strategy not known by the management of the firm proposing the change. We ask whether a firm's management can exploit the capital market's information in deciding either whether to proceed with a contemplated strategy change or whether to continue with a previously initiated strategy change. In the case of a proposed strategy change, we show that managers can extract the capital market's information by announcing a potential new strategy, and then conditioning the decision to implement the new strategy on the size of the market's price reaction to the announcement. Under this arrangement, we show that a necessary condition to implement all and only positive net present value strategy changes is that managers proceed to implement some strategies that garner negative price reactions upon their announcement. In the case of deciding whether to continue with a previously implemented strategy change, we show that it may be optimal for the firm to predicate its abandonment/continuation decision on the magnitude of the costs it has already incurred. Thus, what looks like “sunk‐cost” behavior may in fact be optimal. Both demonstrations show that, in addition to performing their usual role of anticipating future cash flows generated by a manager's actions, capital market prices can also be used to direct a manager's actions. It follows that, in contrast to the usual depiction of the information flows between capital markets and firms as being one way — from firms to the capital markets — information also flows from capital markets to firms.
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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 | 0.005 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".