The Road to Sustainable Investing: Corporate Governance, Sustainable Development Goals, and the Financial Market
Bibliographic record
Abstract
This study investigates the impact of corporate governance (CG) and sustainable development goals (SDGs) practices on financial markets and company performance in Malaysia compared to developed countries like the United States, United Kingdom, Canada, and Singapore. The study uses panel data regression models to analyse the impact of CG and SDG adoption on stock return, volatility, investor sentiment, profitability, liquidity, and solvency from 2017 to 2021. The findings show that CG and SDG practices have a positive impact on financial market and company performance in both developed and developing countries. However, the strength and specific variables of the relationship differ depending on the country context. In developed countries, board responsibilities, remuneration, engagement with stakeholders, SDG4 (Quality Education), and SDG10 (Reduce Inequalities) are positively associated with stock return. In contrast, audit committee effectiveness and SDG8 (Decent Work and Economic Growth), SDG11 (Sustainable Cities and Communities), and SDG13 (Climate Action) are significant in Malaysia and Singapore. The study emphasizes the significance of context-specific factors in determining the effect of CG and SDG practices on financial market and company performance. It recommends Malaysia learn from developed countries’ best practices and adopt a tailored approach to implementation based on its country context.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".