A fuzzy data envelopment analysis model for evaluating the efficiency of socially responsible and conventional mutual funds
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT Although several data envelopment analysis (DEA) models have been proposed in the literature for mutual funds' performance evaluation, few of them incorporate nonfinancial criteria. In this paper a fuzzy DEA model is used, allowing mutual funds relative performance evaluation in a more realistic and flexible way. We examine the efficiency of forty US large cap equity mutual funds based not only on financial variables but also on nonfinancial ones. To achieve this aim, we extend Basso and Funari's mutual funds' ethical level proposing a more reliable fuzzy measure of the social environmental responsibility degree of equity mutual funds. It relies on the corporate social performance of the companies invested in by the mutual funds and on the quality of the management in terms of the transparency and credibility degree of the nonfinancial information provided by the mutual funds. We can conclude that socially responsible mutual funds show better behavior in terms of efficiency than conventional funds.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it