Precious Metal Mutual Fund Performance Evaluation: A Series Two-Stage DEA Modeling Approach
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
This paper documents a new series two-stage data envelopment analysis (DEA) modeling framework for mutual fund performance evaluation in terms of operational and portfolio management efficiency that is implemented to a sample of precious metal mutual funds (PMMFs). In the first and second stage, one-input/one-output and multi-input/one-output settings are used, respectively. In the light of the results, the funds assessed are inefficient in both operational and portfolio management process and in particular, they seem to be more inefficiently operated. The operational management efficiency is correlated with portfolio management efficiency and, therefore, sample funds should give more emphasis on their operational policies to ensure their success in the industry. The research framework may not only benefit PMMFs, but also funds of other classes to quantify their performance and improve their competitive advantages.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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