Bibliographic record
Abstract
We study the interplay between corporate liquidity and asset reallocation opportunities.Our model shows that financially distressed firms are acquired by liquid firms in their industries even when there are no operational synergies associated with the merger.We call these transactions "liquidity mergers," since their main purpose is to reallocate liquidity to firms that might be otherwise inefficiently terminated.We show that liquidity mergers are more likely to occur when industry-level asset specificity is high (i.e., industry-specific rents are high) and firm-level asset specificity is low (industry counterparts can efficiently operate distressed firms' assets).We also provide a detailed analysis of firms' liquidity policies as a function of real asset reallocation, examining the trade-offs between cash and lines of credit.The model makes a number of predictions that have not been examined in the literature.Using a large sample of mergers, we verify the model's prediction that liquidity-driven acquisitions are more likely to occur in industries in which assets are industry-specific, but transferable across industry rival firms.We also verify the prediction that firms are more likely to use credit lines (relative to cash) when they operate in industries in which liquidity mergers are more frequent.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".