Portfolio Choice in the Presence of Personal Illiquid Projects
Bibliographic record
Abstract
Personal projects, such as a private business or the purchase of a home, influence portfolio choice in two ways. First, financial assets can be used to provide diversification against bad outcomes of personal projects. Second, financial assets can be used to provide liquidity to personal projects when these projects are illiquid and individuals have a limited debt capacity. The latter interaction is the focus of our paper. Due to this liquidity consideration, individuals are more risk averse if there is a large penalty for discontinuing or under-investing in the final stages of a project. A large penalty arises when there is strong complementarity between investments at dierent stages, or in projects that require lumpy investments. We provide a theoretical analysis and an empirical investigation of these eects. Using data from the 1995 Survey of Consumer Finances, we show that, consistent with our hypotheses, households which are saving to invest in their own businesses or in their own homes have significantly safer financial portfolios. The impact of the first category is particularly strong. Our findings also help explain why households, in particular younger ones, have larger than expected holdings of safe financial assets.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".