Can investors benefit from corporate social responsibility and portfolio model during the Covid19 pandemic?
Bibliographic record
Abstract
Since late 2019 and throughout 2020, the global economy has been experiencing difficult times due to the outbreak of the lethal Coronavirus (COVID-19). This study looks at the financial impact of this epidemic on the global economy using Malaysian market index i.e., FTSE Bursa Malaysia KLCI before and during COVID-19. Measuring the financial impact of this epidemic on the Malaysia economy may help policy makers to develop measures to avert similar financial catastrophic impacts on the global economy. The study uses Sharpe optimal and naïve diversification model to solve a scenario that factors in the level of corporate social responsibility (CSR) exhibited before and during the epidemic to measure the financial impact on the stock portfolio. The results show that the emergence of COVID-19exacerbated the already weak Malaysian economy. Our findings may help the policy makers in Malaysia to develop and maintain techniques and policies that may mitigate the negative financial impact and handle similar epidemics in the future. Future studies could cover the financial impact of CSR using variable scoring and apply the portfolio model with practical and prevailing constraints.
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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 | 0.006 |
| 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 | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".