The Hazards of Expert Control: Chief Risk Officers and Risky Derivatives
Bibliographic record
Abstract
At the turn of the century, regulators introduced policies to control bank risk-taking. Many banks appointed chief risk officers (CROs), yet bank holdings of new, complex, and untested financial derivatives subsequently soared. Why did banks expand use of new derivatives? We suggest that CROs encouraged the rise of new derivatives in two ways. First, we build on institutional arguments about the expert construction of compliance, suggesting that risk experts arrived with an agenda of maximizing risk-adjusted returns, which led them to favor the derivatives. Second, we build on moral licensing arguments to suggest that bank appointment of CROs induced “organizational licensing,” leading trading-desk managers to reduce policing of their own risky behavior. We further argue that CEOs and fund managers bolstered or restrained derivatives use depending on their financial interests. We predict that CEOs favored new derivatives when their compensation rewarded risk-taking, but that both CEOs and fund managers opposed new derivatives when they held large illiquid stakes in banks. We test these predictions using data on derivatives holdings of 157 large banks between 1995 and 2010.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 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".