Bibliographic record
Abstract
Financing patterns in corporate takeovers and private equity deals over the last twenty years demonstrate the widespread application of Rolnick and Weber’s (1986) extension of one of the most venerable principles of monetary economics: Gresham’s Law. Two currencies, corporate securities (bad money) and cash (good money) circulate together with the former playing the dominant role of par money in times when investors exhibit irrational enthusiasm. Heightened social pressure in the form of herding combined with greater uncertainty about the degree and duration of the overvaluation of securities jointly play the role of transactions costs creating a preference for payment in the par money and most deals are financed with equity or debt securities rather than cash. To illustrate, overvalued bank loans were the most common form of financing in the credit bubble up to 2008 while stock deals predominated in takeover financing during the Internet bubble period of the late 1990s. Normal markets, not characterized by irrational enthusiasm, display the use of both corporate securities and cash in funding takeovers with cash deals (good money) enjoying a premium in the form of more favorable stock market reaction. The analysis provides useful insights for both monetary economists and researchers in corporate finance. For the former, it brings a fresh currency to the interpretation and extension of Gresham’s law relating the classic debate to contemporary, as opposed to historical, events. For the latter, the lesson is that principles of monetary economics can enhance understanding of corporate financing choices.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".