Good deal indices in asset pricing: actuarial and financial implications
Bibliographic record
Abstract
Abstract We integrate into a single optimization problem a risk measure, beyond the variance, and either arbitrage‐free real market quotations or financial pricing rules generated by an arbitrage‐free stochastic pricing model. A sequence of investment strategies such that the couple (expected return, risk) diverges to will be called a good deal (GD). The existence of such a sequence is equivalent to the existence of an alternative sequence of strategies such that the couple (risk, price) diverges to . Moreover, by appropriately adding the riskless asset, every GD may generate a new one only composed of strategies priced at one. We will see that GDs often exist in practice, and the main objective of this paper will be to measure the GD size. The provided GD indices will equal an optimal ratio between both risk and price, and there will exist alternative interpretations of these indices. They also provide the minimum relative (per dollar) price modification that prevents the existence of GDs. Moreover, they will be a crucial instrument to detect those securities or marketed claims that are over‐ or underpriced. Many classical actuarial and financial optimization problems may generate wrong solutions if the used market quotations or stochastic pricing models do not prevent the existence of GDs. This fact is illustrated in the paper, and we point out how the provided GD indices may be useful to overcome this caveat. Numerical experiments are also included.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".