Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the conditions under which discount risk leads to closed‐end funds trading at a discount. Design/methodology/approach A model of investor portfolio choice is developed in which investors face proportional fees for holding managed funds but fixed transaction fees for purchasing other risky assets. The conditions under which investors will hold shares in closed‐end funds are derived. Findings It is shown that, with fixed transaction costs in the market for risky assets, investors with wealth below a certain threshold will hold pooled index funds that charge a proportional fee, rather than the market portfolio chosen by wealthier investors. If a portfolio of closed‐end index funds yields greater volatility of returns to investors than open‐end index funds (i.e. displays “excess volatility”), and charges the same fees, the closed‐end funds need to trade at a discount in equilibrium to attract buyers. The same applies to actively managed funds if higher fees fully reflect extra expected returns from the managers' skill. Practical implications A primary determinant of closed‐end fund discounts is discount volatility and co‐movement across funds. Originality/value Until now it has been argued that discount risk needs to be systematic (correlated with market returns) to be priced. The evidence that discount risk is systematic is weak. There is strong empirical evidence of excess volatility and co‐movement of discounts across closed‐end funds, which in our model are a sufficient condition for funds to trade at a discount, under plausible assumptions. This model thus provides a stronger argument that discount risk explains why discounts exist.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".