Politically Connected Firms and Forward-Looking Disclosure in the Era of Oman Vision 2040
Bibliographic record
Abstract
Oman Vision 2040 is the blueprint for Oman’s future aspirations. This vision is set with a number of high-level long-term targets to reflect the desired progress towards the strategic goals, in order to direct all Omani companies to build productive strategies and innovative plans to diversify the country’s economy and reduce the dependence on the oil sector. All Omani companies are required to move according to this path by disclosing forward-looking information and goals in their annual reports. The progress will be monitored by the Vision 2040 Follow Up Unit which will report regularly on the targets. Therefore, our paper examines whether the presence of ruling family members on boards of directors impacts the quality and tone of forward-looking disclosure (FLD). Based on the sample of 34 Omani financial listed firms on Muscat Stock Exchange between 2014 and 2020, we found that there is a positive and significant association between politically connected firms and FLD quality. This confirms prior literature that politically connected firms are considered more transparent than their non-connected peers. We also found that firms with ruling family board members disclose more good forward-looking news in the chairman’s statements. Furthermore, in the case of poor financial performance firms, we found that ruling members tend to disclose more good news than bad news, and they could use impression management techniques to avoid the negative attraction and to maintain their reputation in the market. From these findings, we draw important implications for policymakers and shareholders who need to encourage firms to appoint ruling family directors on their boards (to a specific extent) due to the potential beneficial outcomes they deliver.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".