Chief Executive Officer Characteristics and Financial Restatements in Malaysia
Bibliographic record
Abstract
The purpose of this paper is to investigate whether the Chief Executive Officer (CEO) characteristics affect the occurrence of financial restatements in Malaysian firms. The CEO characteristics used in this study were tenure, honorific title, gender, expertise, and age. In addition, the financial restatement has been measured as a dummy variable as to whether companies restate their financial statements or not. The sample of this study comprised 442 companies listed in the main market of Bursa Malaysia during the period 2012–2016. The panel data method was utilised to analyse the data. This study employed a logistic regression analysis. The results of this study revealed that there is a positive and significant relationship between CEO tenure and CEO gender with financial restatements. In addition, this study found a negative and significant relationship between CEO honorific title and financial restatements. However, the results found insignificant relationships between CEO expertise and age with financial restatements. This study highlighted the importance of considering CEO characteristics as one of the influential determinants of financial restatements in Malaysian companies.
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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 | 0.027 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".