Debt Investments in Private Firms: <i>Legal Institutions and Investment</i><i>Performance in 25 Countries</i>
Bibliographic record
Abstract
The authors document the types of private debt investments made by fund managers into private firms across 25 countries from 2001–2010. Returns to private debt investments depend on lender (fund manager) characteristics, particularly portfolio size per manager, thus highlighting the role of time allocation for due diligence and monitoring. Also, investment returns are significantly related to borrower (firm-specific) risk. By contrast, market conditions, such as TED spreads, and country-level legal factors, such as creditor rights, are insignificantly or, at most, weakly related to returns. Market and legal conditions are nevertheless significantly related to private debt investment volumes and location. <bold>TOPICS:</bold> <ext-link>Real assets/alternative investments/private equity</ext-link>, <ext-link>fixed income and structured finance</ext-link>, <ext-link>fixed-income portfolio management</ext-link>
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".