Good deal measurement in asset pricing: Actuarial and financial implications
Bibliographic record
Abstract
We will integrate in a single optimization problem a risk measure \nbeyond the variance and either arbitrage free real market quotations or financial pricing \nrules generated by an arbitrage free stochastic pricing model. A sequence of investment \nstrategies such that the couple (risk; price) diverges to (-∞, -∞) will be called \ngood deal. We will see that good deals often exist in practice, and the paper main \nobjective will be to measure the good deal size. The provided good deal measures will \nequal an optimal ratio between both risk and price, and there will exist alternative \ninterpretations of these measures. They will also provide the minimum relative (per \ndollar) price modification that prevents the good deal existence. Moreover, they will \nbe a crucial instrument to detect those securities or marketed claims which are over \nor under-priced. Many classical actuarial and financial optimization problems may \ngenerate wrong solutions if the used market quotations or stochastic pricing models \ndo not prevent the good deal existence. This fact will be illustrated in the paper, \nand it will be pointed out how the provided good deal measurement may be useful to \novercome this caveat. Numerical experiments will be yielded as well.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".