Earnings Opacity and Closed-End Country Fund Discounts
Bibliographic record
Abstract
Closed-end country funds are interesting in that they have two sets of prices for the same underlying assets—the net asset value (NAV) of the fund holdings as measured using the underlying firms’ stock prices in their home markets and the fund price at which the fund trades on a U.S. stock exchange. Utilizing the theoretical framework of information asymmetry in two separate markets for an identical asset, we find that the difference between the fund’s NAV and its trading price (i.e., the fund discount) is positively associated with the earnings opacity of the underlying companies. Such a positive association is consistent with the notion that U.S. investors face higher information acquisition and processing costs when compared with local investors, and therefore earnings opacity exacerbates the information disadvantage of U.S. investors, leading to a larger fund discount. We further show that the positive relation varies predictably with U.S. investors’ information acquisition and processing costs and with the extent to which host stock markets are segmented from the U.S. market. Specifically, we find that the positive relation between earnings opacity and fund discounts is weaker for those funds with more U.S. cross-listings in fund holdings, with underlying companies following financial reporting standards similar to U.S. standards, and with less segmented local markets.
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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 | 0.001 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".