CANADIAN INVESTORS AND THE DISCOUNT ON CLOSED-END FUNDS
Bibliographic record
Abstract
Small investors ' sentiment has been proposed by behaviouralists to explain the existence and behavior of discount on closed-end funds (CEFD). The empirical tests of this sentiment hypothesis so far provide equivocal results. Besides, most of out-of-sample tests outside U.S. are not robust in the sense that they fail to well control other firm characteristics and risk factors that may explain stock return and to provide a formal cross-sectional test of the link between CEFD and stock return. This thesis explores the role of CEFD in asset pricing and further validates CEFD as a sentiment proxy in Canadian context and augments the extant studies by examining the redemption feature inherent in Canadian closed-end funds and by enhancing the robustness of the empirical tests. Our empirical results document differential behaviors in discounts between redeemable funds and non-redeemable funds. However, we don't find supportive evidence of CEFD as a priced factor. Specifically, the stocks with different exposures to CEFD fail to provide significantly different average return. Nor does CEFD provide significant incremental explanatory power,
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".