Does Audit Committee Characteristics Promote Risk Management Practices in Nigerian Listed Firms?
Bibliographic record
Abstract
There has been a huge and deluge of risk threatening industries at an unequalled magnitude in recent times. As such, the board of directors and senior executives are increasingly expected to manage their various organizations' risk portfolios, affecting their financial performance. This has led to the assigning of the risk assessment role to the audit committee. The board of directors and its audit committee play an essential function in Enterprise Risk Management (ERM) by building up the right condition or tone-at-the-top. Given the board's responsibilities for representing the interests of shareholders, it plays a vital role in overseeing management's approach to ERM. This study examined the relationship between audit committee characteristics and risk management of some selected listed firms in a developing country like Nigeria. The study used secondary data to describe the dependent variable (financial risk decomposed into credit risk and liquidity risk) and the explanatory variables (decomposed into audit committee accounting expertise, audit committee meetings, audit committee independence and audit committee gender). The study used pair sample t-test, student t-test, Pearson Moment Correlation and random panel data estimator for twenty (20) selected listed firms for 2012-2016. Findings indicate that there is a negative between audit committee accounting expertise and financial risk. This revealed that Accounting Expertise in Audit Committees are likely to involve in activities and practices to curb financial risk. In addition, the Audit committee meeting indicates a negative relationship with credit risk. Audit committee gender and audit committee independence have a negative effect on liquidity risk. Therefore, this study recommends that Audit committees embrace Enterprise Risk Management (ERM) to manage risks effectively across the organization. Risk management processes should be one of the major points of discussion during audit committee meetings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".