Market governance, financial innovation, and financial instability: lessons from banks’ adoption of shareholder value management
Bibliographic record
Abstract
Abstract As the economy has grown increasingly financialized, the relationship between financial innovation and instability has attracted more attention. Previous research finds that the proliferation of complex financial innovations, like asset securitization and new financial derivatives, helped to erode the market governance arrangements that kept excessive bank risk-taking in check, inviting instability. This article presents an alternative way of understanding how financial innovations and market governance arrangements combine to shape instability. Market governance arrangements also shape how financial firmsreceiveinnovations, leading to greater or lesser instability at particular times and places. I illustrate this argument by tracing the effects of changing corporate governance arrangements at large US banks in the 1990s and 2000s. Like non-financial firms in the preceding decade, banks adopted reforms associated with the shareholder value model of corporate governance. These changes to internal bank governance arrangements affected the agendas of bank executives in ways that encouraged expanded use of securitization and derivatives. Drawing from this case, I argue that a full understanding of instability in the financialized era requires closer attention to the (institutionally-structured) interests of financial innovationusers—not just to features of financial innovations themselves.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| 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".