Endogenous Credit, Business Cycle, and Portfolio Selection
Bibliographic record
Abstract
News story for “Endogenous Credit, Limited Commitment, Business Cycle” Countercyclical Investment Patterns with Endogenously Determined Credit Anticipated returns are sufficiently high to offset the elevated risks during downturns, presenting favorable investment opportunities. For instance, Warren Buffett accumulated more than $10 billion in profits amidst the 2008 financial crisis, as highlighted in his Wall Street Journal interview from October 2013: “In terms of simple profitability, an average investor could have performed just as well investing in the stock market if they bought during the panic period.” In “Endogenous Credit, Limited Commitment, Business Cycle,” Choi, Koo, Lim, and Yoo reveal a divergence in investment behavior between affluent and less affluent individuals during economic downturns. Whereas wealthier individuals tend to increase their exposure to risky investments, less affluent individuals tend to reduce theirs. We employ rigorous modeling and solution analysis to establish endogenous borrowing constraints in general equilibrium, elucidating the observed investment patterns.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".