The Hazards of Expert Control: Chief Risk Officers and Risky Derivatives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it