Three Essays on Information Disclosure
Bibliographic record
Abstract
My thesis consists of three chapters and each chapter studies a different aspect of how information is generated and diffused among different market participants. Chapter 1 and 2 study the impact of timing of 13(f) disclosures. Section 13(f) of SEC regulation requires any financial institution with \$100 million or more in assets to disclose its holdings on a quarterly basis within 45 days after the quarter end. Recently, the SEC was petitioned to shorten this 45-day period. In Chapter 1, I develop a model to examine the impacts of a shortened reporting period. Among other results, I demonstrate that a shorter reporting period results in more liquid markets albeit at the expense of reducing price informativeness. In chapter 2 we look at 14 years of form 13F filings between 1999 and 2012. We demonstrate that active institutions tend to file their holding disclosures with longer delays. We show that concerns about copycat investors do not cause the financial institutions to delay their filings; however, fears of presence of front-runners prompt the financial institutions to file their disclosures with longer delays. We also look at financial institutions' decision to delay around important corporate events for stocks in the institutions' portfolio and document that institutions delay their filings around these events possibly to hide their true voting powers. Chapter 3 studies the implications of SFAS No. 14 and SFAS No. 131, which require firms to disclose the existence of sales to individual customers representing more than 10\% of total firm revenues. We document that firms gain visibility by disclosing economic relationships with reputable trading partners. We find that supplier firms enjoy a boost in news coverage and a subsequent reduction in advertising expense when they disclose trading relationships with well-known customer firms. After relationship establishment, supplier firms are more likely to be held by the same institutional investor and covered by the same analyst following their customer firms. Our findings highlight the role of product-market network as an important channel through which small and young firms gain investor recognition and improve their operating environment.
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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".