Common Ownership in Syndicated Loan Markets and Disclosure of Mutual Fund Performance
Bibliographic record
Abstract
This dissertation investigates the role of financial institutions, which are important external capital providers for firms and exert significant influence on various aspects of corporate behaviors. In Chapter 1, I examine the impact of interconnections among institutional investors on credit markets. In Chapter 2, I study the disclosure practices of mutual funds.Chapter 1 studies whether economic interconnectedness among contracting parties is associated with reduced agency frictions in debt contracting. Using common ownership (CO) among lenders to identify economic connections and a strict research design, I document that when CO between a participant lender and lead lenders is higher, the participant takes a larger share of the loan. The inferences are robust to using financial institution mergers as plausibly exogenous variation in CO and to controlling for alternative mechanisms. In addition, I show that the effect of horizontal CO on the loan structure is more salient when incentives for tacit coordination are stronger. Finally, I find that risk-taking incentives in managerial compensation decrease with vertical CO between the borrower and lead lenders, suggesting that compensation contracts may serve as a plausible channel by which vertical CO reduces agency frictions. Chapter 2 investigates the disclosure of mutual fund performance by focusing on S&P 500 index funds. My examination is motivated by a new SEC rule to promote clear and concise disclosure for fund investors. Based on the guidance and requirements in the SEC rule, I develop a custom measure of information density of management discussion of fund performance (MDFP) in annual shareholder reports. I define information density as the number of facts required by the SEC final rules scaled by the length of the MDFP, showing the extent to which fund managers deliver key information within a reasonable length of text. I provide evidence that information density predicts future fund flows and reduces investors’ reliance on past fund performance. Further, investors rely more on fund disclosure instead of past performance when the economic uncertainty is lower and when the relative importance of fund disclosures is higher (i.e., signals of past performance are weaker and disclosure of performance is more credible).
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 | 0.041 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".