Performance of Auto-Callable Reverse Convertibles, Information Disclosure Prescribed by Regulation S-K Change in 2013 Under U.S. Security Act: an Empirical Study
Bibliographic record
Abstract
This thesis studies the effect of the estimated value disclosure imposed in 2013 on the realized return of the auto-callable reverse convertibles (ACRCs) in the U.S. retail market. The sample of this study consists of about 3,700 issues of ACRCs during the period from 2011 to 2015, which is collected from the Edgar database of the U.S. Security and Exchange Committee (www.sec.gov). The comparison between product realized return and the return of underlying assets reveals that the ACRCs are underperformed by 5% on average, while further analysis shows that the return difference was broadened after the disclosure regulation. It is found that the statistical attributes of the underlying assets are critical to the product performance while they are hidden by the issuer of ACRCs. The disclosure regulation is presumed to enhance information disclosure and to further protect the investors, but the deteriorated performance of ACRCs indicates a failure of the regulation. To protect the anonymity and confidentiality, the identity of the issuer of ACRCs in our sample is removed without compromising the validity of our research. The original data is available upon request.
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 | 0.004 | 0.018 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".