The Social Construction of Market Value: Institutionalization and Learning Perspectives on Stock Market Reactions
Bibliographic record
Abstract
This study advances a social constructionist view of financial market behavior. The paper suggests that the market's reaction to particular corporate practices, such as stock repurchase plans, are not, as financial economists contend, simply a function of the inherent efficiency of such practices. Rather, stock market reactions are also influenced by the prevailing institutional logic and the degree of institutionalization of the practice. The theory first predicts that the emergence of the agency perspective on corporate governance in the mid-1980s represented a powerful new institutional logic that would lead the market to reverse its prior aggregate reaction to stock repurchase plans in the United States. The paper then considers the potential for institutional decoupling of repurchase plans and develops competing hypotheses about how the market value of these policies might have changed as more firms formally adopted, but did not implement, the plans over time. In contrast to a financial economic perspective on market valuation, which suggests that markets should discount the value of a policy as evidence of non-implementation accumulates, this study posits that institutionalization processes might increase the market value of a policy as more firms adopt it, despite growing evidence of decoupling. Implications for institutional theory and theoretical perspectives on capital markets are discussed.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".