Strategic Pricing Behavior under Asset Value Maximization
Bibliographic record
Abstract
In this paper, we empirically evaluate strategic pricing behavior and shareholder delegation of financial market objectives in the U.S. butter and margarine market. Shareholders are assumed to maximize the value of firm assets according to a certainty equivalent capital asset pricing model (CAPM). The delegation setup instructs managers to maximize a linear combination of firm profits and shareholder equity values. A key result from the analysis is that a test of financial market influences cannot be rejected in the product market for butter and margarine. Product market competition is best described using a conjectural variation model of industrial organization. Estimates of Lerner Indexes suggest significant market power and counterfactual simulations point toward significant biases in estimated Lerner Indexes when capital market dimensions are ignored or if the wrong market structure is assumed. Dans le présent article, nous avons effectué l'évaluation empirique de comportements stratégiques dans la détermination des prix et la délégation d'objectifs financiers de la part d'actionnaires sur le marchéétatsunien du beurre et de la margarine. Nous avons supposé que les actionnaires maximisent la valeur de l'actif d'une firme selon l'équivalent certain du modèle d'évaluation des actifs financiers (MEDAF). La structure de la délégation amène les gestionnaires à maximiser une combinaison linéaire des profits de la firme et de la valeur nette des actions. L'un des principaux résultats de l'analyse est qu'un test des influences du marché des capitaux sur le marché des produits ne peut être rejeté pour le cas du beurre et de la margarine. La concurrence sur le marché des produits est plus adéquatement caractérisée par un modèle d'organisation industrielle à variations conjecturales. Les estimations de l'indice de Lerner ont indiqué la présence de pouvoir de marché, et les simulations contrefactuelles ont montré des biais significatifs dans les valeurs estimées des indices de Lerner lorsque l'on ne tient pas compte du marché des capitaux ou que l'on suppose une structure de marché erronée.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".