A multi-period decision framework for mutual fund efficiency: integrating range directional DEA with machine learning for dynamic investment optimization
Bibliographic record
Abstract
This study introduces a novel multi-period decision framework to enhance the analysis of mutual fund efficiency. Traditional evaluation methods are often static and struggle to handle the negative data common in volatile markets, limiting their practical utility. To overcome these limitations, our framework synergizes a dynamic Range Directional Measure (RDM) Data Envelopment Analysis (DEA) with a predictive machine learning engine combining Support Vector Machines (SVM) and the Gray GM(1,1) model. This hybrid approach enables not only a more accurate assessment of management and investment efficiencies with complex datasets but also generates robust, forward-looking performance predictions. Empirical validation confirms the framework’s high predictive accuracy and its effectiveness in supporting dynamic investment strategies. The study provides fund managers and stakeholders with a sophisticated and adaptive tool for portfolio optimization in complex, multi-period decision environments.
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How this classification was reachedexpand
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".