Global financial crisis after ten years: a review of the causes and regulatory reactions
Bibliographic record
Abstract
Purpose The purpose of this paper is to review the relevant literature on the causes of and regulatory reactions to the financial crisis of the last decade, popularly known as the “Global Financial Crisis (GFC)” or the “Housing Crisis” in the USA. Design/methodology/approach This review primarily focuses on the four main causes of the crisis, namely, excessive household leverage, securitization, corporate governance and credit ratings. The main reactionvis-à-visrecovery measures taken by most governments were quantitative easing (QE), bailouts and more stringent regulations of banks, though the discussion mainly focuses on QE. Findings In this paper, the authors summarize the literature on the causes and regulatory reactions to the GFC and propose future avenues of research for various topics. Originality/value Research on the GFC spans multiple disciplines as well as multiple facets of financial economics. A review paper such as this should help future researchers in generating ideas and gathering information for their research. Given that no review uncovers all worthy papers, the authors apologize in advance to the authors of any papers that the authors have inadvertently not reviewed in this paper.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".