Trend Breaks and the Persistence of Closed-End Fund Discounts
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Bibliographic record
Abstract
Abstract Closed-end fund (CEF) prices often exhibit large and persistent deviations from their associated net asset values (NAVs). This occurrence is puzzling, given that NAVs are openly accessible to the public for CEFs, which essentially consist of repackaged financial assets. The persistence of these deviations is particularly notable when using linear models, suggesting the need for nonlinear models to comprehend this phenomenon known as the CEF discount puzzle. To unravel this puzzle, we employ the RALS-LM framework, enabling the identification of multiple endogenously chosen trend-breaks, and conduct an analysis utilizing data from 31 CEF discounts. Our findings reveal that CEF prices tend to fluctuate around time-varying trends, which aligns with the characteristics of regime switching models. Additionally, we demonstrate that incorporating non-normal errors through moment conditions enhances efficiency at the margin. Moreover, we establish that nonlinearity solely in the form of level shifts falls short in explaining the persistent nature of CEF discounts.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it