Measuring the voluntary disclosure of graphical information in annual reports using a new disclosure index
Bibliographic record
Abstract
The use of graphs in disclosing financial information in corporate annual reports represents a significant dimension of management’s disclosure strategy.This study extends previous research into financial graphs by documenting the nature and extent of graphs use among the 2003 corporate annual reports of the top 50 and bottom 50 listed Malaysian companies ranked by net profit before tax.A disclosure index known as Graphical Information Disclosure Index (GIDI) is developed to measure the level of graphical disclosure.Additionally, the research examines whether companies with ‘good’ and ‘poor’ performance disclose graphs differently; and whether the voluntary disclosure of graphical information can be explained by signalling theory.Drawing on signalling theory, six hypotheses are developed and tested.Sixty-four per cent of companies use graphs; the mean number is 5.3, with plantation companies using the most graphs.The most commonly graphed financial variables are sales, profit, EPS and shareholders’ fund. Evidence is found that graph use is contingent upon favourable performance.Using a wider industry classification, this study also finds a significant difference in the level of disclosure by different industry sectors.Comparison with prior single-country studies reveals that graphs are used less extensively in Malaysia than in the U.S.A., Canada, U.K. and Australia; but more extensively than Hong Kong.The implications of these findings are considered and areas of further research discussed.
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 | 0.008 | 0.053 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".