Analysis of the Effectiveness of Classical Models in Forecasting Volatility and Market Dynamics: Insights from the MASI and MASI ESG Indices in Morocco
Bibliographic record
Abstract
This research evaluates the effectiveness of traditional models in predicting movements in the Moroccan financial market, with a focus on the MASI and MASI ESG indices. As environmental, social, and governance (ESG) criteria gain prominence in financial analysis, this study examines the strengths and limitations of conventional predictive models. The findings reveal a significant correlation between the two indices while underscoring the challenges traditional models face in effectively integrating extra-financial dimensions, particularly environmental and social factors. These limitations hinder their ability to fully capture the complexities of the Moroccan financial market, where ESG considerations are increasingly shaping economic trends. Given these constraints, the study emphasizes the need for more advanced forecasting tools, particularly models that comprehensively incorporate ESG factors. Such advancements would enhance the understanding of ongoing economic transformations and address emerging challenges. By refining these tools, predictive models could become more relevant and better equipped to meet the specific demands of Morocco’s evolving financial landscape.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".