Análise de evidências de dinâmica caótica e não linear aplicada ao mercado financeiro
Bibliographic record
Abstract
The present study contributes to the theory of asset pricing by analyzing evidence of non-linearity and chaos in the time series of daily returns from March 30, 2009 to December 31, 2013 provided by Bloomberg of the following countries and indices: India - CNX Finance, Germany - Dax All banks, United States - kbw bank, Brazil - IFNC, Australia - asx 200 financials, France - cac financials, Mexico - BMV, UK - nmx8350, Canada - TSX financials and Russia - Moscow Exchange Financials. As to the specific objectives of the study we have: 1) to analyze if the database in question can be classified in the light of efficient market theory, that is, if the returns of the assets follow a stochastic process, 2) use the BDS statistic to verify whether the series in question are linear or not, and 3) to verify the existence of Chaos in the database through the Liapunov Peak Expo. The Lyapunov maximum exponents were calculated by the mutual information method at the mean, and t = 1 was found for all series and the estimation of the immersion dimension was calculated by the method proposed in Cao (1997). From the obtained results, it was verified that the series in question are non-linear and also can not be considered chaotic, and these should be treated as stochastic and non-linear processes.
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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 | 0.006 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 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".