Un modèle du coût du financement et du ratio d'endettement des entreprises non financières
Bibliographic record
Abstract
The main aim of this paper is to calculate the cost of financing for Canadian non-financial businesses and to develop a model to explain financing cost trends on the basis of selected macroeconomic variables. The model described herein is a system based on four equations: one for the real after-tax cost of financing; one for the real rate of return required by creditors and another for the real rate of return required by shareholders (these two rates are linked to, among other things, the firms' debt ratio); and, lastly, an equation for the optimal debt ratio, which is derived from conditions of cost minimization. We also show that, for certain parameter values, our model conforms to the Modigliani-Miller propositions (1958), according to which the cost of financing is independent of the debt ratio, which therefore does not affect firms' investment decisions. Our work is based on two empirical observations. The first is that the cost of financing is relatively stable with respect to real interest rates. In our model, interest-rate movements directly influence the cost of debt, although the link between real interest rates and the rate of return on (or cost of) equity is quite weak. Since equity represents, on average, 60 per cent of total financing, the reasons why the cost of financing varies much less than interest rates are obvious. The second interesting observation is that the cost of financing and the debt ratio of firms appear to be positively linked to the rate of inflation. In our model, these two variables are affected by inflation because of the asymmetric nature of the tax system. Given current tax parameters, our results indicate that a 1-percentage-point drop in the expected inflation rate reduces the cost of financing by 8 basis points and the debt ratio by 1.2 percentage points. However, these results are contingent upon one of our working assumptions—that investors in Canadian businesses are individuals residing in Canada.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".