Bibliographic record
Abstract
This thesis explores the role of governance and sponsorship on mutual fund characteristics like advisory contracts, fees and returns. My first and second essays are related to board governance for a special type of mutual funds called closed-end funds (CEFs herein). Unlike corporates and open-end funds, CEFs have unique characteristics like similarity in size and complexity along with compulsory public filings which make them an exemplary laboratory for an examination of questions dealing with corporate finance and investments. \nIn June 2004, the SEC required mutual fund boards to reveal additional information about the inputs and processes involved in advisory contract approvals to help investors make more informed decisions and to encourage independent directors to act more independently when negotiating advisory fees. Using a hand-collected governance panel database of all U.S. closed-end funds (CEFs) during 1994-2013, we examine the effect of this change on advisory fees. We find that the percentage of independent directors is significantly and negatively related with advisory fees only after this event. The maximum (minimum) numbers of advisory fee decreases (increases) occur in the year after the 2004 SEC amendments. We find that advisory-rate decreases are significantly more likely for a CEF with a higher percentage of independent directors after but not before the event even after controlling for post-event board structure changes. Overall, our results support the idea that the 2004 SEC amendments successfully encouraged independent fund directors to act more independently in negotiating advisory fees with fund advisors. \nUsing the same governance data, the second paper explores how CEF governance affects expense ratios, returns and premiums. Independent directors are more effective in monitoring and influencing fund performance measures that are less complex and more directly controllable. The results suggest that CEFs with higher board ownerships are better aligned with shareholders’ interests. Ownerships of directors are positively and significantly associated with most variables that are expected to indicate greater value from the monitoring of directors. Using a dynamic panel two-step system generalized method of moments estimator, the results are robust in the presence of endogeneity concerns (reverse causality, unobserved heterogeneity and simultaneity). \nIn the third essay, we focus on mutual fund governance at the sponsor level. Taking advantage of the wide variety of sponsors in the Canadian mutual fund market, we examine how different fund sponsorships affect measures of fund performance. We find that cost minimization and manager abilities are important drivers of performance differences among Canadian fixed-income funds differentiated by sponsor and fund types.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".