Enterprise Risk Management Program Quality: Determinants, Value Relevance, and the Financial Crisis
Bibliographic record
Abstract
This paper investigates factors associated with high‐quality Enterprise Risk Management ( ERM ) programs in financial services firms, and whether ERM quality enhances performance and signals credibility to the financial markets. ERM , developed with the assistance of the accounting profession, provides a framework and plan to integrate management of all sources of risk. Challenged by measurement difficulties common to research on management control systems, prior ERM studies present mixed findings. Using ERM quality ratings of financial companies by Standard & Poor's, we find that higher ERM quality is associated with greater complexity, less resource constraint, and better corporate governance. Controlling for such characteristics, we find that higher ERM quality is associated with improved accounting performance. Results show a market reaction to signals of enhanced management control from initial ERM quality ratings and rating revisions, and a stronger response to earnings surprises for firms with higher ERM quality. Focusing on the recent global financial crisis, our analysis suggests that there is no relation between ERM quality and market performance prior to and during the market collapse. However, returns of higher ERM quality companies are higher during the market rebound. Overall, results reveal that firm performance and value are enhanced by high‐quality controls that integrate risk management efforts across the firm, enabling better oversight of managers' risk‐taking behavior and aligning that behavior with the strategic direction of the company.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".