Maximin investment problems for discounted and total wealth
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Bibliographic record
Abstract
We study an optimal investment problem for a continuous-time incomplete market model such that the risk-free rate, the appreciation rates and the volatility of the stocks are all random; they are not necessarily adapted to the driving Brownian motion, and their distributions are unknown, but they are supposed to be currently observable. The optimal investment problem is stated in ‘maximin’ setting which leads to maximization of the minimum of expected utility over all distributions of parameters. We found that the presence of the non-discounted wealth in the performance criterion (in addition to the discounted wealth) implies an additional condition for the saddle point of the maximin problem: the saddle point must include the minimum of the possible risk-free return. This is different from the case when the utility depends on the discounted wealth only. Using this result, the maximin problem is reduced to a linear parabolic equation and minimization over two scalar parameters. It is an important development of the results obtained in Dokuchaev (2002, Dynamic Portfolio Strategies: Quantitative Methods and Empirical Rules for Incomplete Information. Boston: Kluwer; 2006, IMA J. Manage. Math., 17, 257–276).
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 it