Maximizing the Probability to Reach the Goal: An Exploration Exercise in Goal-Based Wealth Management
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Goal-based wealth management (GBWM) is a portfolio approach in which the investor associates risk with the probability of not attaining a financial goal. Using several datasets, the author examines the performance of a multiperiod GBWM strategy that maximizes the probability of achieving a financial goal. With varying restrictions about leverage and short sales, he compares the goal-based wealth investor with a standard and a goal-attentive mean–variance investor. Without transaction costs, the results suggest that, in terms of goal achievement, a goal-based wealth investor focusing on the probability of reaching a goal does better than a standard mean–variance investor. Compared to a goal-attentive mean–variance investor, the results still favor the goal-based wealth investor but to a lesser extent. With transaction costs, goal-based wealth and goal-attentive mean–variance investors yield similar results in many cases.
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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 | 0.007 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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