Does Ethical Investment Perform Differently?\nA Comparative Study on SRI Index Performance in North America
Bibliographic record
Abstract
Socially responsible investment has increased rapidly in growth in the last few years and it has attracted great attention on their financial performance. \nThis study investigates the comparative performance of SRI index relative to the conventional index in both Canadian and U.S. market. The entire sample period has been divided into two sub-periods - Pre-crisis and Post crisis - in order to examine the influence of the recent financial crisis on the performance of the indices. In addition to the traditional finance measures of Sharpe, Treynor and Jensen measures, the econometric methods of OLS regression with dummy variables and GARCH (1, 1) models are also applied as the performance measurements. \nThe main finding of this study indicated that the incorporation of social screening process does not affect the financial performance. Specifically, the results imply the SRI indices do not perform differently from respective conventional indices. In addition, the SRI indices tend to have a positive implication. Therefore, investors will not be financially sacrificed for investing in an ethical index. Whilst the recent financial crisis had a great influence on the volatility, it had no significant effect on the average return of all studied indices. It can be noted that there is a difference between the Canadian and U.S. capital market in terms index performance.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".