Improving pension funds’ performance using data envelopment analysis considering government regulations
Bibliographic record
Abstract
Pension fund managers operate in an investment environment with strict government regulations and a unique taxation system. Also, low birth rates, together with a higher average age of the population and an increase in general life expectancy provide further motivation for investigating pension funds’ performance. Adding to the study by Badrizadeh and Paradi (2020) in which a new model was presented for evaluating pension funds’ performance considering the effects of invisible variables, this study introduces a new methodology based on data envelopment analysis (DEA) which evaluates the pension funds’ performance by considering the importance of different variables based on an expert’s judgements as well as borrowing useful information from the mutual funds’ dataset. Similar variables between pension funds and mutual funds are included. The correlation between mutual fund variables is extracted and tested statistically. Then, these regressions are used to define trade-offs in the pension funds’ model. When these trade-offs and expert’s opinions are added, the results show that the discriminatory power of the DEA increases. Furthermore, three different target levels are defined for inefficient pension plans. This research is applied to Canadian pension funds and mutual funds but could be utilized in similar problems in industry and government
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".