A clustering approach to examine the dynamics of the NASDAQ topology in times of crisis
Bibliographic record
Abstract
This paper investigates the dynamics of the NASDAQ topology before, during, and after 2008 financial crisis.First, multiresolution analysis by virtue of wavelet transform is employed to denoise each NASDAQ sector return series.Second, the correlation matrix of sectors is built and analyzed in each time period to view comovements of sectors.Third, hierarchical clustering trees are constructed in each time period to find out how the structure of the NASDAQ market evolves through time.Our results suggest that interrelationships between sectors become stronger in times of crisis and especially in post-crisis period.In addition, some markets tend to form the same cluster in all time periods; for instance the Industrial and Bank sectors and the Telecommunication and Computer sectors.However, the general topology of the NASDAQ market has been considerably changed over periods.In sum, the complex structure of the NASDAQ market is dynamic and is more integrated after 2008 financial crisis.This result indicates that there are less diversification opportunities in the post-crisis period in comparison with pre-crisis period.These empirical findings are important for the development of subsequent portfolio strategies.
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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 | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".