Bibliographic record
Abstract
ABSTRACT The mean–variance optimization (MVO) model of Harry M. Markowitz is the foundation of quantitative portfolio construction and asset allocation. While Markowitz originally developed MVO for forming portfolios of tradable assets in isolation, it has been adapted for creating portfolios of tradable assets in the presence of non‐tradable assets and liabilities. In a series of publications, Paul D. Kaplan and Thomas M. Idzorek further extend MVO to the household economic balance sheet. The extended MVO model is the net worth optimization (NWO) model. In NWO, human capital is modeled as an asset mix held long, and, as in surplus optimization, liabilities are modeled as an asset mix held short. NWO operates in terms of net worth returns rather than in the returns on the financial assets. Kaplan and Idzorek maximize an approximation for expected utility developed by Haim Levy and Harry M. Markowitz. In this article, I show how to achieve this by combining existing quadratic programming techniques with nonlinear equation solution techniques in a novel way. I also discuss and demonstrate how NWO differs from the discretionary wealth approach (DWA) introduced by Jarrod Wilcox.
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How this classification was reachedexpand
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".