Quis Custodiet Ipsos Custodes? Revisiting Rating Agency Regulation
Bibliographic record
Abstract
The phrase, neither a borrower nor a lender be, seems to have fallen out of favour in modern Canadian society, if it was ever heeded in its native context. In the modern market economy, access to loans, debentures, and other forms of debt capital can be an integral ingredient in a successful business venture. A business entity's access to capital, and investors' ability to provide that capital, is limited by the business's credit rating. Yet, this rating is determined by unregulated external credit rating agencies (CRAs) paid by the issuers, which encourages the possibility of agency cost market inefficiency, because rating agents may prefer their own interest to those of the debt market. While some authors argue that agencies' reputations can substitute for regulation, this paper will demonstrate that this passive belief may be na?ve. Effective, comprehensive regulation would ensure that any rating agency in a conflict of interest - or perpetuating a potential conflict - will be sanctioned by strict liability. This will prevent rating agencies' clandestine and oligopolic access to information from allowing them to conceal their opportunism with impunity. The recent 2007 sub-prime mortgage disaster again demonstrates how CRAs' decisive role in the market, without regulation to ensure their transparency and accountability, leads to abuses and - ultimately - to disaster. Despite the earlier lessons in Enron, as of early 2007, CRAs have continued to display languor in the face of impending crisis, while doling out top ratings to their paying customers - the very corporations they rate. To substantiate the latter assertions, this paper will examine the following issues: 1) rating agencies' structure and function in the market; 2) rating agencies' modi operandi; 3) the current regulatory system governing rating agencies; and 4) proposed regulatory amendments.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".