Bibliographic record
Abstract
Stock market reactions to financial reports have been extensively studied in previous years and for various markets. However, not much research has been conducted regarding the reaction of global financial markets to integrated reporting. This study examines the market reaction to the publication of integrated reports for a sample of 316 global companies for the reporting year 2018 by using an event study methodology. The results of the applied event study indicate significant cumulative average abnormal returns (CAARs) after the publication date. To ensure robust estimation results, we use a modern asset pricing model, namely, the three-factor model according to Fama and French (1993). For comparability purposes, we also estimate the average cumulative abnormal returns using a market-adjusted model, a capital asset pricing model (CAPM), and a Fama-French model taking generalized autoregressive conditional heteroskedasticity (GARCH effects) into account. In addition, a cross-sectional analysis is conducted. We find a significant positive CAAR on days one to four after the publication day of the integrated report compared to a negative CAAR for financial information disclosure. Our results suggest that investors react to information provided in the integrated report and that they react differently to the release of integrated report information than to financial information. Furthermore, our cross-sectional analysis confirms that companies with a significant positive CAAR around the publication date show certain characteristics. It was found that European companies have a higher likelihood to experience a stronger significant positive market reaction to their integrated report publication. It was also found that firms in the consumer defensive, financial, industrial, and real estate sectors are more likely to experience a positive market reaction. No significant differences were found for companies of a larger size or with a higher profit margin. Ultimately, this confirms that integrated reporting affects company value. This is the first event study for a multi-country sample applying multi-factor models for nonfinancial disclosure event studies including a cross-sectional analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".