Written for MFIN 6692.0 under the direction of
Bibliographic record
Abstract
I would like to sincerely express my appreciation to my supervisor Dr. J. Colin Dodds for his guidance, help and encouragement to complete this thesis. I also would like to thank all professors in the MFin program for giving me the opportunity to study finance and cultivate the ability to solve problems. In addition, I would like to give my special thanks to my friends who gave me a lot of advice and help when I did the research. Most importantly, I would like to extend my love and thanks to my parents. ii The correlation between risk and return of Canadian mutual funds based on VAR By Nuozhou Zhang This paper uses VAR to measure the risk of Canadian mutual funds and to determine the correlation between the risk. And I built two equations to do the analysis. I use Equation 3.2 to expose the correlation between the return and the potential loss of mutual funds which can be illustrated on the coefficient 1. This equation will also expose whether the previous risk still has influence on the mutual fund returns, what the correlations are, and how long this influence will last. This information will illustrated on the coefficient 2, 3
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.662 | 0.502 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".