Bibliographic record
Abstract
Hedge funds have become an increasingly important asset class in recent years. This paper discusses the asset allocation decision of an investor who is considering investing in hedge funds. We develop a simple procedure that may help in making this decision when the assets consist of a core equity portfolio, the risk-free asset, and a hedge fund. A regime-switching framework is used to model the joint returns of the hedge fund and the equity market. We use monthly intervals so that the regimes can change only at most once a month. Within each regime the returns on the two risky assets are bivariate lognormal with constant parameters. These parameters are estimated from the empirical data. We show how to determine the optimal allocation of an investor, such as a pension plan, to hedge fund assets. Our procedure is based on the maximization of expected utility, and we use different horizons. We restrict the admissible strategies to buy-andhold strategies, so we do not allow for portfolio balancing. We illustrate the procedure with examples. We find that bias in the hedge fund expected return has an important impact on the results. We note that some hedge fund strategies have substantial left-tail risk to which investors may be very averse. This type of risk aversion is not adequately captured by the standard expected utility model, but it could be added as a constraint.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".