Do the Underlying Portfolios Matter? A Comparative Study of Equity-Linked Pay-at-Maturity Principal Protected Notes in Canada and the UK
Bibliographic record
Abstract
This study examines the relationship between the return and the holding cost of equity-linked pay-at-maturity principal protected notes (EL-PAM-PPNs) and the mean return and volatility of the underlying portfolio using 1568 EL-PAM-PPNs issued in the UK and Canada between 2003 and 2015. We find that: (i) the underlying portfolio’s mean return decreases the note holding cost; (ii) the underlying portfolio’s volatility increases the note return and decreases the note holding cost; (iii) investors could maximize note return and minimize holding costs by choosing EL-PAM-PPNs prudently. Investors in both countries should purchase notes with higher participation rates, where the underlying portfolio contains a higher number of stocks and lower expected volatility. UK investors should avoid callable notes and choose notes with a longer time to maturity, where payoff is determined by a single observation of the underlying portfolio’s value at maturity. Surprisingly, Canadian investors should choose callable notes and notes with a shorter time to maturity, where payoff is determined by the average of multiple observations of the underlying portfolio’s value over the life of the note. They should also look for notes that include a guaranteed positive return, and where the underlying asset has a higher dividend yield.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".