Do Bubbles Have Real Effects? Balance Sheet Analysis and Review of Literature
Bibliographic record
Abstract
This paper studies the nature and the existence of bubbles in financial markets. Do bubbles have real effects? How do they behave? From balance sheet, we learn that the impacts of bubbles depend on who owns the bubbles assets. The effects of speculative bubbles are intensified when it is banks that hold financial assets. Banks’ balance sheets are improving following the expansion of a bubble that sharply increases asset prices, earnings and equity. The increase in equity rises the capacity of banks to grant credit to the economy, to stimulate economic growth, investments and production. Balance sheet crises, in which asset prices collapse, pose particular economic challenges. Banks’ equity fall abruptly, Banks may not be able to grant as much credit to the economy as in the past. Economic activity is contracted, there will be a reduction in investment and production. A financial crisis can then cause an economic crisis. The total assets of banks can change greatly, depending on whether we are in periods of spectacular increases in asset prices or in periods of drastic fall in assets prices. We use econometric methods to determine the specific effects to each bank, and we note that in presence of speculative bubbles, the total assets held by the largest banks increases on average by about US $360.5 billion. In contrast, during periods of drastic declines in asset prices, the total assets held by the world’s largest banks decreases by about US $291.3 billion. Thus, over time, after a business cycle (recovery, recession, recovery), the total assets of the world’s largest banks increase by an average of US $90 billion.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".