When does improper conduct become criminal? Conflicts of interest in the mutual fund industry
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
Purpose To emphasize the need for financial services companies such mutual funds and brokerage houses to establish internal controls and related procedures for identifying potential conflicts of interest. Design/methodology/approach Reviews government investigations into scandals involving the mutual fund industry over the past two years, including late trading, marketing timing, revenue sharing, directed brokerage, and gift‐giving; notes that criminal prosecution in this area is infrequent but still possible; and recommends, given the current landscape, that financial services companies examine their procedures for identifying and eliminating conflicts of interest. Findings Concludes that recent mutual fund scandals have changed the regulatory landscape and the regulators, and in some cases prosecutors, are committed to aggressively pursuing any possible impropriety or conflict of interest between mutual fund advisors and the investing public. Originality/value Provides a useful review of recent mutual fund scandals for the purpose of demonstrating to fund managers and directors why they should review their controls and related procedures to identify and eliminate conflicts of interest.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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