The Cost of Debt and Its Economic and Financial Drivers: An Empirical Study of European Companies
Bibliographic record
Abstract
This study aims to define the relationship between the cost of debt and firm financial performance measures. To this end, a multiple linear regression model has been applied on a sample of European companies issuing bonds traded on Euronext. The analysis considers data relating to the period 2019-2023. Specifically, the work tests the impact of measures concerning company size, liquidity, solidity and profitability on the yield to maturity. The independent variables are the following: net working capital to total assets ratio, return on assets, logarithm of revenues, leverage, interest coverage ratio, current ratio and the coefficients of variation of operating profit and net income. Consistent with the literature on this topic, size has a significant and inverse impact on the cost of debt. For the other variables, the regression also returns coefficients in line with expectations. The resulting model allows for estimating the cost of debt by applying the coefficients to a company’s financial data. Even if the company is not publicly traded, the relationship returns a measure of its debt capital cost. This study contributes to the research on the cost of debt, a central topic in corporate finance. It also provides practical implications for corporate management by raising awareness of the drivers that influence the cost of debt capital and offering the opportunity to optimize financial decisions.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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".