ESG Signals, Investor Psychology and Corporate Financial Policy: A Bibliometric Study
Bibliographic record
Abstract
This study undertakes a systematic literature review combined with bibliometric analysis to examine how abnormal returns are studied in relation to environmental, social, and governance (ESG) factors, investor sentiment, and dividend policy. Using RStudio version 2025.09.0+387 and VOSviewer version 1.6.20, we conduct a bibliometric study that integrates performance analysis, science mapping, and network analysis. The dataset consists of 532 publications published between 2000 and 2025 and indexed in the Web of Science and Scopus databases. Our results show that scholarly work on abnormal returns is organised around three main thematic areas. First, investor sentiment is closely linked with event study applications, behavioural finance explanations, and sentiment analysis, which underscores the importance of psychological influences in understanding market anomalies. Second, prior studies on dividend policy continue to rely heavily on event study designs to evaluate how markets react to dividend announcements. Third, investor sentiment and dividend policy are connected through their common focus on abnormal returns, which operate as a central conceptual link between these strands of literature. Although interest in behavioural and policy-related determinants of abnormal returns has grown over time, work that explicitly incorporates ESG considerations remains relatively marginal. This peripheral position points to an important gap, suggesting that the dynamic relationships among ESG performance, investor sentiment, dividend decisions, and abnormal returns are still not fully explored. The contribution of this study lies in bringing these elements together by mapping research on event studies while treating ESG performance as a potential market signal that may shape both investor sentiment and corporate financial policy.
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 | 0.014 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.225 | 0.274 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".