A statistical investigation of the returns on closed-end investment companies
Bibliographic record
Abstract
The common shares of closed-end funds, unlike mutual funds, trade on the stock exchanges. A market determined value of the assets of closed-end funds (net asset value) is published weekly for those funds listed on the New York Stock Exchange. A discrepancy exists between the market price of the common share of the closed-end fund and the net asset value per common share of the fund. The size of these discrepancies, premiums and discounts, has never been adequately explained within the context of financial theory. Furthermore, estimates of risk coefficients (betas) are such that the common equity appears les risky than the closed-end fund itself. An investigation was undertaken of the statistical properties associated with both weekly and monthly market value and net asset value return series for twelve closed-end funds listed on the New York Stock Exchange from 1965 to the end of 1972. These twelve funds account for approx imately fifty percent of all funds by asset size listed during that period. Non-parametric tests demonstrated a lack of independence in contiguous observations and some additional support was given by a measure of serial correlation. Goodness-of-fit tests were performed for the normal distribution and it was rejected as representative of the data. The distribution of the return series, as verified by the sample moments, is leptokurtic and shows properties consistent with a stable distribution. The lack of independence and normality in the data causes serious violations of the assumptions necessary to fit the market model in order to estimate the betas of the closed-end funds. The violations are such that the market return betas are likely to be seriously underestimated and therefore cause the common equity of closed-end funds to appear less risky than the funds themselves. Some support for theory which indicates that the common equity should be riskier is given by the results of the lagged market model.
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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 | 0.003 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".