Bibliographic record
Abstract
(LSIFs) are a special investment vehicle for Canadian retail investors. Aside from being eligible as RRSP contribu- tions, a unique benefit of investing in LSIFs is the 30% tax credit investors receive for their investment in these funds. Given this unique benefit, the LSIF industry has had little trouble attracting investors, and total assets under management in these funds have grown rapidly since they were first introduced in the 1980s. However, as shown in Table 1, these funds have underperformed other investments available to Canadian investors. 2 Why have these funds performed so poorly? What are the key elements in the organization of these funds that may have contributed to such poor performance? This paper argues that fund manager compensation in LSIFs creates a gap between the interests of investors and managers. This misalignment may have played a critical role in managers' investment decisions and subsequent fund per- formance. The relative poor performance of LSIFs may be a function of several key elements in manager compensa- tion. First, the average management expense ratio (MER) for LSIFs is considerably higher than for other investment funds (see Table 1). Unlike mutual funds, the stated management fees of LSIFs may not cover all fees charged by fund managers. 3 Second, the investment so their investment horizon is effectively eight years or longer. In contrast, managers of LSIFs have a much shorter investment horizon since their performance and fees are evaluated annually or more frequently. In addi- tion, incentive fees are structured as if the fund man- agers are granted a reload option on the fund's assets. Once the fund's performance exceeds the target return during an evaluation period, the manager exercises the option and collects the incentive fee. The option is immediately reloaded and exercisable in the next eval- uation period. Fourth, incentive fees charged by most LSIFs are asset-based, whereas investors derive their returns from the performance of the entire portfolio. Finally, unrealized investment gains are frequently used in the evaluation of fund performance. Flat manage- ment fees are based on the fund's net asset value (NAV), which is a function of unrealized gains. Most LSIFs also use unrealized gains to determine whether or not incentive fees are earned or the amount of incentive fees to be charged. Using a Monte Carlo simulation model, we assess the total fees charged by a typical LSIF in excess of the
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.288 | 0.079 |
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".