Fair value's effects on closed-end funds' discounts and premia: is level 3 the sole perpetrator?
Bibliographic record
Abstract
Purpose This study examines the long-term link between fair valuation uncertainty and discounts/premia in closed-end funds. This study argues that, in exploring the close-end funds puzzle, prior research generally omits to consider the uncertainty surrounding the measurement of funds' financial disclosure, as reflected in the fair value hierarchy, when investment specialty differs across funds. Design/methodology/approach Regressions were employed to explore how the fair value hierarchy affects closed-end funds' discounts/premia when investment specialty differs. The authors also examine the effects pre- and post-2012 to explore if that relationship changes due to the additional disclosure requirements enacted at the end of 2011. Findings The authors find that the three levels of the fair value hierarchy have effects that vary according to a fund's specialty. For equity specialized funds, Level 3 significantly increases discounts and decreases premia, suggesting the impact of valuation uncertainty that underlies Level 3 estimates; this relationship disappears (decreases in severity) for premia (discount) experiencing funds post-2012. In contrast, Level 1 and Level 2 do not have any significant effect on discounts or premia except that post-2012, Level 2 begins to display discount decreasing effects. For bond specialized funds, no significant association was noted between premia and any of the fair value levels except that post-2012, Level 3 begins to display premium increasing effects. However, results are different for discounts. The authors note that Level 1 valuations significantly increase discounts, but only post-2012; Level 2 valuations significantly decrease discounts (pre- and post-2012), consistent with such estimates incorporating unique and relevant information; and Level 3 valuations do not have a significant effect on discounts. Originality/value The results of this study revisit prior evidence and indicate that results about the effects of fair value measurement and the closed-end funds' puzzle are sensitive to the period length being considered and the investment specialty of the fund. The authors also note that additional disclosure regarding Level 3 valuation inputs decreases market concern for valuation uncertainty and increases the liquidity benefits of investing in Level 3 carrying funds.
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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 | 0.006 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".