The Effect of Auditor’s Industry Specialization on the Quality of Financial Reporting of the Listed Companies in Tehran Stock Exchange
Bibliographic record
Abstract
This study examines the effect of auditor’s industry specialization on quality of financial reporting of the listed companies in Tehran Stock Exchange during the period of 7 years from 2008 to 2014. It is expected that industry specialist auditors will show more competence and auditing quality in discovering opportunistic behavior in executives and most probably they will report financial statements to maintain their reputation; in other words, it is expected that auditors specialized in industry will have an effective role in corporate governance and improving the quality of financial reporting. In this research, the accurate of predicting future cash flows operations through components of the operation profit was served as a measure for the quality of financial reporting and patters of the market share based on the total audited properties of the company and total auditor income was used as auditor expertise characteristics in that audited unit's industry were used. A total number of 119 companies were selected as samples and using logit regression model, the results were analyzed. The findings suggest that auditor's expertise in the industry, has a direct impact on the quality of corporate financial reporting. In this regard, testing the research's hypotheses showed that the auditor expertise in the industry (on the basis of market share pattern based on auditor's total revenue) has no significant effect on the quality of financial reporting. However, if the auditor expertise in the industry (on the basis of market share pattern based on the sum of the audited assets) was to be measured, it will leave a significant effect on the quality of financial reports. Therefore, it is concluded that the factor of the auditor's expertise in the industry is sensitive in relation with the type of indices used to assess it.
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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 | 0.001 | 0.007 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".