Do internationally cross‐listed non‐US firms obtain more favorable terms for syndicated loans?
Bibliographic record
Abstract
Purpose The purpose of this paper is to study the impact of cross‐listing and cross‐listing location on the terms of the private debt of firms not located in the USA. Specifically, the paper examines the empirical relationship between three syndicated loan terms (pricing, maturity and amount) at loan initiation and the cross‐listed status of the borrower (cross‐listed in the USA, UK, through depository receipts or not at all), while (not) differentiating between the stage of economic development of the borrower's home country. Design/methodology/approach The three loan terms are modeled as a simultaneous system of equations and are estimated on a very extensive sample of 3,883 observations. The impact of endogeneity biases due to the sequential choices to and where to cross‐list are examined using the inverse Mill's ratios from a bivariate probit model. Findings All else held equal, foreign borrowers that are cross‐listed directly in the UK obtain loans with higher spreads, longer maturities and larger loan amounts if they are from economically developed countries. Borrowers from emerging economies pay lower spreads but receive shorter maturities on syndicated loans if cross‐listed in the UK. Cross‐listings in the USA are not associated with any significant differential impacts on the three loan terms. Originality/value This paper makes an important contribution to the cross‐listing and capital structure literatures by providing evidence that the net benefit from being cross‐listed for one debt component of the cost of capital (i.e. syndicated loans) depends on the listing destination and upon whether or not the borrower is from an emerging economy. The paper provides practical guidance to corporate financial officers on the benefits of international cross‐listing and the choice of cross‐listing venues on the terms of private debt issues.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".