THE DETERMINANTS OF RISK PREMIUM: THE CASE OF BANK LINES OF CREDIT GRANTED TO SMEs
Why this work is in the frame
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Bibliographic record
Abstract
The goal of this paper is to gain a better understanding of the factors that determine the risk premium on bank lines of credit obtained by SMEs, and whether firm size, which may be used by banks to segment their client base, also has an impact on credit costs. An analysis of data from 406 SMEs in Canada showed the main determinants of risk premium were firm size, line of credit size, ability to repay, the relationship between banker and entrepreneur, and the length of the relationship with the bank. These determinants change according to the market segment in which the potential borrower operates. Banks appear to use a transactional approach for smaller borrowers, where credit terms are based on quantitative financial data, and a relational approach for larger firms, where relationship length and quality become significant.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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 it