An analysis of the relation between enterprise risk management (ERM) information disclosure and traditional risk measures in the US banking sector
Bibliographic record
Abstract
The purpose of this article is to validate the quality and the relevance of enterprise risk management (ERM) information disclosure by analyzing the relation between the different dimensions of ERM disclosed in the annual report and the traditional measures of risk in the US banking sector. We use content analysis to measure ERM dimensions and a correlation analysis to document the links between risk exposure, consequences, and strategies (Aebi, Sabato, & Schimd, 2012), and the traditional measures of risk (Schnatterly, Clark, Howe, & DeVaughn, 2019) disclosed in the annual reports from 2006 to 2009. We then separately make the analysis for the period before and after the crisis to identify any effect of the crisis on ERM information’s ability to predict and reflect the banking sector’s traditional risk (Maingot, Quon, & Zéghal, 2018). Our results reveal the overall validity of ERM information in assessing traditional risk measures through a significant correlation between ERM exposure, consequences and strategies, and most of the traditional measures of risk. Finally, we confirmed the relevance and the robustness of our results through a portfolio analysis approach. This research sheds new light on the relevance of ERM information by introducing a new framework and a new methodology for assessing the validity of this information within the banking sector, where risk management plays a vital role. The results are potentially useful for banks regulators as well as for producers and users of the information on banking risks.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".