ESG Indices Efficiency in Five MENA Countries: Application of the Hurst Exponent
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The efficient market hypothesis (EMH) is one of the main theories related to financial markets. This hypothesis is based on the idea that stock prices already reflect all available market information. In its weak form, the EMH states that future prices cannot be predicted by analyzing historical asset prices. This paper aims to test the effectiveness of environmental, social and governance (ESG) indices in the Middle East and North Africa region (MENA) and compare them with their conventional counterparts. The sample data covers the period from September 27 2018 to December 23 2021 in daily frequency. Our empirical approach is based on Hurst behavior using the R/S statistic. The results reject the market efficiency hypothesis for both ESG and conventional indices and show that these indices are significantly inefficient with persistent returns. In terms of the level of efficiency between the ESG and conventional indices, the study does not indicate significant differences.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it