Is prior performance priced through closed‐end fund discounts?
Bibliographic record
Abstract
Abstract In open‐end mutual funds (unit trusts), there is a strong positive cross‐sectional relationship between net inflows to individual funds and past performance, as if investors attributed performance to managerial skill. Performance shows only very weak persistence, however, so at first sight investors do not appear to gain anything by responding to past performance information. This behaviour can be explained by the fact that past performance is effectively unpriced in the unit trust market, since management fees are unresponsive to demand. If investors believe that there is a non‐zero probability that future performance will turn out to be positively correlated with past performance (i.e. that there is an element of managerial skill in performance), but a zero probability that this correlation will be negative, it is rational to prefer funds with better past performance when performance is not priced. In other words, it costs nothing to insure against the possibility of some managerial skill effect. If this explanation of the flow–performance relationship in unit trusts is correct, one would expect the relationship between investor demand and past fund performance to be much weaker if past performance were to be priced. We test this hypothesis in the market for closed‐end funds (investment trusts). Because closed‐end funds do not trade at net asset value, but at a price determined in the market, strong demand will raise the ratio of price to net asset value (known as the premium). Since it is well established that premiums are mean‐reverting, future shareholder returns on funds currently on high premiums tend to be depressed by the reversion of the premium to the mean. In the closed‐end fund market, as for open‐end funds, there is little evidence of performance persistence, and therefore, to the extent that funds with good past performance are pushed to higher premiums, the expected return on them is less than on the average fund. This implicit pricing mechanism should mean that demand is a declining function of the premium, so that, even if demand is an increasing function of past performance for a given premium, any effect on the premium itself will be muted. We test this hypothesis for closed‐end funds traded in the US and the UK. We find that there is a statistically significant effect of past performance on the premium in both countries. However, consistent with the hypothesis, it has limited economic significance, since it represents only a small component of premium variability. Copyright © 2008 John Wiley & Sons, Ltd.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 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".