Human Capital, Asset Allocation, and Life Insurance
Bibliographic record
Abstract
Financial planners and advisors have recently started to recognize that human capital must be taken into account when building optimal portfolios for individual investors. But human capital is not just another pre-endowed asset class that must be included as part of the portfolio frontier. An investor's human capital contains a unique mortality risk, which is the loss of all future income and wages in the unfortunate event of premature death. However, life insurance in its various guises and incarnations can hedge against this mortality risk. Thus, human capital affects both the optimal asset allocation and the optimal demand for life insurance. Yet historically, asset allocation and life insurance decisions have consistently been analyzed separately both in theory and practice. In this paper, we develop a unified framework based on human capital in order to enable individual investors to make both decisions jointly. We investigate the impact of the magnitude of human capital, its volatility, and its correlation with other assets as well as bequest preferences and subjective survival probabilities on the optimal portfolio of life insurance and traditional asset classes. We do this through five case studies that implement our model. Indeed, our analysis validates some intuitive rules of thumb but provides additional results that are not immediately obvious.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 source (direct Gemma or distilled Codex), 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".