Combination of ESG and momentum : evidence of the Canadian market
Bibliographic record
Abstract
The increasing relevance of Environmental and Social impact of firms on the world has also increased the importance of the ESG score. According to the study performed by Kaiser and Welters, (2019), a higher ESG score decreases the probability that the company is going to be affected by a social movement or any kind of adversity that the market could face. This study analyzes if a double-sorted momentum strategy based on ESG scores and prior returns can outperform and achieve a lower volatility than the Canadian market benchmark and single factor momentum portfolios between the years 2008 and 2020. At the same time, it is tested if the strategy also decreases the volatility of momentum during market crashes. I find that the double-sorted strategy outperforms the S&P/TSX Composite in most of the portfolios created. My strategy achieves an average return higher than the benchmark and the single factor momentum portfolios. However, the volatility that the double-sorted portfolios present is higher than the benchmark or single factor portfolios, this can be noticed in the fact that the strategy achieves higher maximum return values, but the minimum returns are considerably lower than the single factor strategies or the benchmark. Focusing on market crashes, the strategy still presents a higher volatility with average returns higher than the S&P/TSX Composite or the single factor portfolios.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".