Portfolio Selection with Multiple Time Horizons: A Mean Variance—Stochastic Goal Programming Approach
Why this work is in the frame
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Bibliographic record
Abstract
Standard approaches to portfolio selection from classical Markowitz mean-variance model require using a time horizon of historical returns over a period that the investor defines in a conventional way. To avoid arbitrary choice of the time horizon, this paper proposes a satisfying compromise solution relying on mean variance—stochastic goal programming (EV-SGP), where the goals are defined from the different time horizons under consideration. As the information on returns provided by each horizon is of different quality and reliability, critical parameters in this method are Arrow's absolute risk aversion (ARA) coefficients and the investor's preferences for each horizon. After formulating the proposed method, a suitable technique to determine the ARA coefficients in our context is given in a strict way according to Arrow's risk theory. An actual numerical example is developed throughout the paper leading to consistent results. The sensitivity analysis shows robust solutions. A generalization of results requires further examples.
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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 | 0.001 | 0.003 |
| 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