Investors’ Valuation for Asset Liquidity and the Corporate-Treasury Yield Spread
Bibliographic record
Abstract
The present study seeks to contribute to the explanation of the non-default component within corporate-U.S. Treasury yield spreads. This is done by extending the model by Krishnamurthy and Vissing-Jorgensen (2012), assuming that investors value not only U.S. Treasuries' liquidity but instead value liquidity independently from the underlying asset. For that purpose I modify a standard asset pricing model by allowing certain groups of assets to directly contribute to investor's utility. Empirical tests of the model's implications confirm this view and show that changes in the holdings of most liquid assets cause a stronger impact on corporate-Treasury yield spreads compared to changes in the holdings of least liquid assets. Finding this systematic pattern, points to the existence of a demand function for liquidity. Further, I provide evidence that changes in the holdings of liquid assets are priced separately from commonly used controls for credit default risk, as well as from controls measuring an asset's market liquidity.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".