The Interactive Effect of Ownership Structure on the Relationship between Annual Board Report Readability and Stock Price Crash Risk
Bibliographic record
Abstract
This study investigates the interactive effect of ownership structure on the relationship between annual board report readability and stock price crash risk in companies listed on the Tehran Stock Exchange (TSE). The negative skewness model was used to measure the crash risk of stock prices and the Fog index was used for determining the readability of the board of directors’ report. The ownership structure is examined in institutional ownership, significant managerial ownership, and family ownership. The data of companies listed on the TSE from 2013 to 2019 have been used. The statistical method of this research is multiple regressions and, to test the research hypotheses, the data panel model and the ordinary least squares method have been employed. Overall, this study provides new evidence to explain the reporting quality and the crash risk of stock prices from the lenses of the agency theory. It further investigates the interactive effect of ownership structure on the relationship between annual board report readability and stock price crash risk. The results show a significant correlation between the readability of the board of directors’ report and the crash risk of stock prices. Furthermore, the relationship between the readability of the board report and stock price crash risk is not affected by the ownership structure, including institutional ownership, significant managerial ownership, and family ownership. It can be inferred that an ownership structure, which includes institutional shareholders, significant shareholders, and family ownership, increases the supervision of managers and their reports, so they cannot keep adverse information from being released. This will ultimately improve the readability of their reports and reduce the risk of stock price crashes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".