Heteroscedasticity of deviations in market bubble moments – how the goods and bads lead to the ugly
Bibliographic record
Abstract
We conjecture that market bubbles may be the results of the interplay of Goods and Bads (toxic products) which develop through three interlocking moments – herding, swarming and stampeding, with deviations marked by heteroscedasticity. We use our stylized model of financial predation, the Consolidated Model of Financial Predation, and data we have accumulated through in-the-field eight-year research and the study of 30 years of U.S. market history in order to explore the foundations of market crises. We find that blind trust (or the positivity bias) and of the fear to miss out on an opportunity to enter/exit a market impacts the investors’ decisions to invest or retract. We show how markets are driven towards a make-or-break predatory dynamic that creates winners and losers due in part to weak regulations and identify a constant k that permeates market behaviours.
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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 | 0.004 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| 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".