Strategic Complementarities and Money Market Fund Liquidity Management
Bibliographic record
Abstract
Following the financial crisis, there has been increased regulatory focus on the management of liquidity in mutual funds and, specifically, whether funds hold enough liquidity to guard against the potential for investor runs. Using a novel, detailed regulatory dataset on the portfolio holdings of US money market funds, I find that internal prime money market funds—those that manage the liquidity of other funds in the fund family—have lower liquidity than external prime funds. This suggests that money market funds hold more liquidity to reduce the potential for strategic complementarities (i.e., incentives to run) in investor redemptions, because the family funds that invest in these internal funds should be able to coordinate their redemption decisions. Additionally, at quarter ends, when non-US bank dealers reduce their repo funding (Munyan, 2015), I find that prime money market funds reduce their overnight liquidity, which consists primarily of overnight repos. External prime money market funds do not let this decreased cash demand from non-US bank dealers reduce their liquidity as much as internal funds do. This all suggests that these external prime money market funds are more concerned about overnight liquidity, consistent with greater concern about potential investor strategic complementarities.
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 | 0.010 |
| 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 | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".