Performance analysis of sustainable stock indices against conventional ones: an empirical investigation of G7 countries
Bibliographic record
Abstract
The study evaluates the performance of sustainable indices in comparison to conventional indices of G7 countries for the period starting from 1st January 2015 to 30th September 2022. The article employs risk-adjusted measures such as Sharpe, Treynor, Jensen's alpha, Modified Sharpe, and Sortino, which shows that sustainable indices of most countries like Canada, Japan, Germany, and Italy highlighted superior performance and the investors earned positive rewards for bearing incremental risk. However, the stressed time of crisis is proved to be a penalty for socially ethical investors. The Fama Decomposition model shows that premium rewards earned by sustainable indices helped the superior-performing countries to secure top ranks. The conditional volatility of sustainable index is measured using the GARCH(1,1) model. The study will benefit the investors to diversify their investments in sustainable indices to earn creditable returns and the financial market professionals in framing policies to uplift the investment in sustainable indices.
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How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".