Environmental, Social, and Governance Disclosures and Market Reaction of Thai-Listed Companies in the Alternative Capital Market
Bibliographic record
Abstract
The primary aim of this research is to investigate the influence of environmental, social, and governance (ESG) disclosures on the market reaction companies listed in Thailand’s alternative capital market, specifically the Market for Alternative Investment (MAI). This interest stems from the growing body of ESG literature in Thailand. This study analyzes 555 corporate annual reports from 111 firms within the MAI, spanning from 2017 to 2021, to measure ESG disclosures through content analysis. The average common share price is used as a proxy for market reaction. Descriptive analysis, correlation metric, and multiple regression are used to analyze the data. The findings reveal that the most common ESG disclosures are social disclosure, governance disclosure, and environmental disclosure. Additionally, there is a noticeable increase in ESG disclosures over the study period. Underpinned by signaling theory, this study finds that governance disclosure positively affects market reaction, while environmental disclosure has a negative impact. Social disclosure shows no significant relationship with market reaction. The implication of this study is that ESG disclosure is crucial for firms due to its significant impact on investors’ investment decisions. Regulators can use the findings in several ways, such as establishing policies to promote or regulate governance disclosure that positively affects market reactions, providing guidelines for companies on effectively disclosing ESG information, communicating quality information, and building investor confidence.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".