Securitization: The “curse” of the Financial System during the 2008 Crisis
Bibliographic record
Abstract
During the 2008 financial crisis, many investment and commercial banks were hit hard. The primary reasons that caused the financial crisis were the abuse of mortgage securitization and the housing bubble. In this paper, our group mainly focused on the potential issue of the mortgage securitization structure and the impact that the abuse of securitization caused. Our group analyzed the correlation between mortgage defaults through the OLS linear regression model. It showed an underestimation of the possibility of defaulting on securitization products (MBS, CDO, etc.). Furthermore, our group analyzed the expansion of this potential risk due to the raised housing bubble. After analyzing the potential risk, our group analyzed the impacts that the securitization caused in macro and micro views. First, our group analyzed vital factors that affect bank performance using the OLS linear regression model. Moreover, based on the change of these critical factors caused by the exposed potential risk from the securitization structure, our group analyzed the bank’s performance during the financial crisis. Second, our group analyzed the macro impacts before, during, and after the financial crisis due to the abuse of securitization. Overall, according to the potential risk in the securitization structure and the macro and micro impacts caused by the exposed potential risk, our group concluded that the abuse of securitization was the “curse” for the whole financial system at that time.
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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 | 0.001 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| 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".