Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate whether a firm’s undertaking of a bond IPO influences the monitoring of the private loans granted to the firm by private lenders. If it does, in which direction the monitoring changes? Design/methodology/approach The author uses both univariate and multivariate analyses to test the hypothesis. For the purposes of this research, the author’s primary data sources are LPC Dealscan, which provides data on private loans; Mergent FISD, which provides data on public bond issues; and the Compustat Industrial Annual Database, which provides the required financial data for the sample firms. The author’s sample covers non-financial US firms for the period of 1991-2010. The author’s final sample consists of nearly 23,000 private loans granted to about 5,500 non-financial US firms. Findings The major finding of this research is that private lenders increase their degree of monitoring of loans that they extend to a firm after it issues a bond IPO. The results of the two-stage bond IPO anticipation model further strengthen the findings. The evidence suggests that as the firm issues public debt for the first time, private lenders get concerned about the potential increase of agency problems and leverage, and consequently, find it valuable to increase the degree of monitoring of loans. Also, the magnitude of change in monitoring is strongly influenced by the degree of information asymmetry, leverage, profitability, and potential to waste free cash flow. Originality/value This paper enhances one’s understanding of the contracting dynamics between private lenders and the firm as it issues in the public debt market. The findings can aid firms anticipate the borrowing conditions they will face if they undertake a bond IPO. Further, the cross-sectional analysis on covenant changes from pre- to post-bond IPO period identifies specific firm characteristics that impact the magnitude of change of covenant intensity and comprehensiveness. As a result, uncertainty regarding post-bond IPO outcomes is reduced for borrowing firms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".