Sentiment-Labeled ESG News and Stock Movement Data of IDX ESG Leaders (Q1 2025) Constituents
Bibliographic record
Abstract
This dataset contains labeled ESG (Environmental, Social, and Governance) news headlines and corresponding stock price data for publicly listed companies included in the IDX ESG Leaders Index during the first quarter of 2025 (Q1 2025). It was developed to support research on the influence of ESG-related news sentiment on short-term stock price movements in the Indonesian capital market.The dataset consists of 30 variables and includes:Raw and translated news headlines related to ESG disclosures from multiple verified media outlets.Cleaned and preprocessed text versions in English, optimized for input into NLP models such as FinBERT.Manual sentiment labels (positive, negative, neutral) assigned to each news item, alongside model-generated sentiment probabilities and polarity scores.Stock price data of the related companies from 5 days before (D-5) to 5 days after (D+5) the news publication date.Calculated cumulative returns across multiple event windows, including [-5,0], [0,+1], [0,+5], and [-1,+1].This dataset is valuable for:Event study analysis on ESG disclosure impacts,Sentiment analysis research in financial contexts,Training or evaluating NLP models in Bahasa Indonesia–English ESG financial domains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.014 |
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".