Board Composition and Accounting Conservatism: The Role of Business Experts, Support Specialist and Community Influentials
Bibliographic record
Abstract
Abstract In this study, we examine the relationship between accounting conservatism and board composition. We categorise outside directors according to their skills, abilities, connections and knowledge in three different categories: business experts, support specialists and community influentials. We address three main questions: Is the financial and accounting expertise of directors relevant to improving accounting conservatism? Does specialised expertise in the board affect the speed at which news is reflected in earnings? And how do the political ties of directors affect the sensitivity of earnings to bad news? Our sample consists of active US biotech firms publicly traded on the NYSE, AMEX and NASDAQ stock exchanges during the 2005–2013 period. Our study confirms that not all outside directors are equally effective in monitoring and contracting and that certain kinds of outside directors, such as politicians, can even lower the sensitivity of earnings to bad news. Our robustness analysis confirms that these results are not conditional on the accounting measure, and suggest that distinguishing directors according to their skills and abilities is crucial to understanding the way in which firm boards affect conservatism.
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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 | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".