G-8 Ülkelerinde Etkin Piyasa Hipotezinin Test Edilmesi: Fourier Kırılmalı Birim Kök Testlerinden Yeni Kanıtlar
Bibliographic record
Abstract
The aim of this study is to test the validity of the efficient market hypothesis for stock market indices in G-8 countries (USA, Germany, France, England, Italy, Japan, Canada, and Russia). The augmented Dickey-Fuller (ADF), residual augmented least squares (RALS)-ADF, Fourier-ADF, and Fourier-Kapetanios-Snell-Shin (Fourier-KSS) unit root tests are used to do this. The longest possible period was used as the analysis period by considering the data available for each index. Unlike the literature, the validity of the efficient market hypothesis for stock market indices in the G8 countries has been comprehensively and comparatively examined by simultaneously considering Fourier breaks, non-normal distribution, and non-linearity. According to the empirical findings, the null hypothesis was not be rejectable for all the unit root tests that were applied to the stock market indices from Germany, France, and Japan. In other words, strong evidence has been obtained for the validity of the efficient market hypothesis for the stock market indices from these three countries. On the other hand, the null hypothesis was rejected for Russia’s stock market index as a result of the unit root tests applied apart from ADF, with the results that emerged indicating the efficient market hypothesis to be invalid for that case. Different results were observed to have been caused by taking into account Fourier breaks and nonlinearity in other indices. The Fourier-KSS test considers the nonlinearity in a dataset using Fourier breaks and was found to provide more evidence for the invalidity of the efficient market hypothesis compared to the other types of tests. This reveals the importance of choosing the appropriate test for the data.
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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 | 0.009 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".