The Impact of Voluntary Disclosure Level on the Cost of Equity Capital in an Emerging Capital Market: The Case of the Amman Stock Exchange
Bibliographic record
Abstract
The impact of disclosure levels on the cost of equity capital is a central issue in contemporary accounting research. Economic theory suggests that higher disclosure reduces information asymmetry and estimation risk, thereby lowering the cost of equity capital. However, empirical evidence on this relationship remains limited, is largely focused on developed markets (e.g., the USA and Canada), and reports mixed results. This study examines the impact of voluntary disclosure on the cost of equity capital in the Amman Stock Exchange as an example of an emerging capital market. The analysis is conducted by regressing the cost of equity capital on disclosure levels and other firm characteristics. Disclosure level is measured using a self-constructed disclosure index based on voluntarily disclosed information in the 2000 annual reports of a sample of non-financial companies listed on the Amman Stock Exchange. The index comprises three main groups and nine categories. Overall, voluntary disclosure is found to be relatively low, with approximately 81% of the 62 disclosure items exhibiting disclosure levels below 50%. Disclosure practices vary across categories, with background information being the most frequently reported and projected information the least reported. The cost of equity capital is estimated using the residual income model proposed by Gebhardt et al. (2001). The findings show that the average required rate of return is about 10%. The findings reveal a significant negative relationship between voluntary disclosure levels and the cost of equity capital. Firms with higher levels of disclosure experience a reduction in their cost of equity capital ranging between 0.067% and 0.083% compared to less forthcoming firms. Among the disclosure categories, background information and market data exert the strongest influence in explaining variations in the cost of equity capital for Jordanian firms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".