Banks' voluntary disclosure in the audit committee reports, cost of equity and the mediating role of financial analysts
Bibliographic record
Abstract
Voluntary disclosure in the audit committee report is expected to provide additional information about the activities undertaken to protect investors. The Securities and Exchange Commission's (SEC) ultimate aim in initially promulgating audit committee disclosure requirements was to reduce firms' cost of equity. However, prior research finds that voluntary disclosure in the audit committee report is akin to impression management. In 2015, the SEC issued a concept release encouraging audit committees to provide additional voluntary disclosures in their reports beyond mandatory requirements. In that context, this paper analyses the effect of the audit committee voluntary disclosure on the cost of equity, with financial analysts playing a mediating role. The sample comprises the top US bank holding companies from 2006 to 2015. We manually code the voluntary disclosure in audit committee reports using a scoring grid. Results show that audit committee voluntary disclosure increases the cost of equity. In addition, the association between voluntary disclosure and the cost of equity is mediated by financial analysts. Hence, we infer that the impression management undertone of voluntary disclosures affects financial analysts' coverage and forecasting properties, which in turn lead to an increase in the cost of equity. The paper's empirical evidence highlights the effects of impression management disclosure by analysing corporate governance voluntary disclosures, cost of equity and financial analysts and brings the issue to the attention of banking regulators, SEC and investors.
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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 | 0.007 | 0.063 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".