The effect of CEO characteristics on financial reporting timeliness in Saudi Arabia
Bibliographic record
Abstract
The purpose of this paper is to examine the effect of some demographic characteristics of the Chief Executive Officer (CEO) on Financial Reporting Timeliness (FRT) in Saudi Arabia. More particularly, this study aims to test whether or not CEO characteristics, namely, tenure, accounting financial expertise, and sociability are associated with FRT. The sample of this study consists of 119 non-financial firms listed on Tadawul Stock Exchange for a period of four years (2014-2017). We use panel regressions and two proxies of FRT. Our findings report that a long-tenured CEO is associated with timely financial reports when the IFRS transition is simultaneously considered. This result implies that companies with a long-tenured CEO reduce the period taken to prepare and disclose their financial reports in the period of IFRS transition. Our findings show that CEO accounting financial expertise is significantly associated with timely financial reporting. This result implies that companies with a CEO who is an accounting financial expert reduce the period taken to prepare and disclose their financial reports on the capital market website. Our findings also report that CEO sociability is significantly associated with timely financial reporting in all instances. This result suggests that companies with a more sociable CEO reduce the period taken to prepare and disclose their financial reports on the capital market website. This result implies that when the CEO is more sociable via social media, firms tend to be more active in disclosing their annual reports timely. Overall, findings report that CEO characteristics do matter regarding the timeliness of financial reporting. Results are robust to an alternative measure of financial reporting timeliness. Our findings should be of interest to policymakers and regulators in Saudi Arabia in formulating new policies as they need to play a role in ensuring the shorter gap of financial report delay. The findings of this research have also a practical implication for shareholders and boards of directors in selecting a new CEO by taking into consideration their accounting financial expertise and their sociability on social media. Findings of this research contribute to the growing literature by examining the effect of CEO characteristics on timely reporting in Saudi Arabia, an understudied and unique context. The present study also complements the recent literature on the determinants of financial reporting timeliness by providing evidence that the sociability and accounting financial expertise of top leaders improve the financial reporting timeliness.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".