Stock Selection by Means of DEA and Stochastic Dominance
Bibliographic record
Abstract
This paper investigates the problem of statistical testing for second order stochastic dominance (SSD) relations among a set of stocks. The SSD rule is the desirable condition that all risk averse, non-satiated investors seek. Due to the fact that SSD is not significantly confirmed by statistical tests, in most cases, weak dominance of some of stocks over others cannot be rejected at a high level of confidence (95% or higher). For this reason, we have tested the necessary rules of SSD efficiency incorporated into data envelopment analysis (DEA) models, recently proposed, on SSD statistical test results to remove the stocks that are weak SSD efficient. Weak SSD efficiency refers to stocks that are never dominated by others and reported as SSD efficient, but they are dominated by a combination of other stocks given the necessary rules of SSD efficiency. Based on an empirical study of 68 financial stocks from the S&P 500 Index, six stocks were determined to be SSD efficient. Applying DEA, this number was reduced to three. These three stocks are desirable choices for the class of investors who prefer having more wealth to less and are risk averse.
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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 | 0.002 | 0.002 |
| 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 | 0.000 | 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".