Evidencia De Comportamiento Caótico En Indices Bursátiles Americanos [Evidence Of Chaotic Behavior In American Stock Markets]
Bibliographic record
Abstract
This article validates the chaotic behavior in the Argentinean, Brazilian, Canadian, Chilean, American, Peruvian and Mexican Stock Markets using the MERVAL, BOVESPA, S&P TSX COMPOSITE, IPSA, IGPA, S&P 500, DOW JONES INDUSTRIALS, NASDAQ, IGBVL and IPC Stock Indexes respectively. The results of different techniques and methods like: Graphic Analysis, Recurrence Analysis, Temporal Space Entropy, Hurst Coefficient, Lyapunov Exponential and Correlation Dimension support the hypothesis that the stock markets behave in a chaotic way and rejected the hypothesis of randomness. Our conclusion validates the use of prediction techniques in those stock markets. It’s remarkable the result of the Hurst Coefficient Technique, that in average was of 0,75 for the indexes of this study which would justify the use of ARFIMA models among others for the prediction of such series.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Tests for chaotic dynamics in stock market indices; the 'validation' is of a market hypothesis, not of research methods.
This analyzes chaotic behavior in stock markets rather than the research system.
Econometric chaos analysis of stock indexes including Canada among many; finance, not research system.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".