COMPARISON OF ALTERNATIVE UTILITY FUNCTIONS IN PORTFOLIO SELECTION PROBLEMS
Why this work is in the frame
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Bibliographic record
Abstract
This paper examines the effect of alternative utility functions and parameter values on the optimal composition of a risky investment portfolio. Normally distributed assets are the setting for the theoretical and empirical analyses. The results agree well with the available theory and imply utility functions and parameter values that are appropriate for investors with particular risk-bearing attitudes. The results give strong empirical support to the proposition that utility functions having different functional forms and parameter values but “similar” absolute risk aversion indices have “similar” optimal portfolios. These results suggest that over horizons up to one year one can safely substitute “convenient” surrogate utility functions for other utility functions, for reasons of tractability or otherwise. The results also provide guidance regarding the significance of the magnitude and change of particular numerical values of the risk aversion index. Moreover, theoretical (“exact”) results are obtained using Rubinstein’s measure of global risk aversion.
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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 | 0.001 | 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 | 0.001 |
| 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