On the role of the chief risk officer and the risk committee in insuring financial institutions against litigation
Bibliographic record
Abstract
Purpose In most financial institutions, chief risk officers (CROs) and their risk management (RM) staff fulfill a role in managing risk exposures, yet their lack of involvement in the governance has been cited as an influential factor that contributed to the financial crisis of 2007-2008. Various legislative and regulatory bodies have pressured financial firms to improve their risk governance structures to better weather potential future crises. Assuming that CROs and risk committees are given sufficient power to influence the corporate governance of financial institutions, can CROs and risk committees protect financial institutions from violating litigable securities law? Can they improve bank performance? The paper aims to discuss these issues. Design/methodology/approach The authors employ a principal component analysis to construct a single measure that captures various aspects of RM in a firm. The authors compare the risk governance characteristics of sued firms with their non-sued peers and consider one of the final outcomes of risky behavior: shareholder litigation. The authors compute ROA and buy-and-hold abnormal returns to capture operating and stock performance and examine whether risk governance improves bank performance by reducing litigation risk. Findings Proper risk governance reduces a firm’s litigation probability. The addition of the RM factor to models that have been previously proposed in the literature improves the accuracy of those models in identifying companies that are most susceptible to class action lawsuits. Better RM improves the financial and stock price performance of financial institutions. Research limitations/implications The data collection is laborious as the information about CRO governance has to be hand-collected from the 10-K report. A broader sample employing, e.g., non-US banks may provide additional insights into the relationship between RM practices, shareholder litigation, and bank performance. Practical implications The study shows that a bank’s RM functions play a critical role in improving bank and operating performance and in reducing shareholder litigation. Banks should emphasize the RM function. Originality/value This is the first study to examine the mechanism behind the positive association between RM and bank performance. The study shows that better RM improves overall bank performance by decreasing litigation risk.
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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 | 0.019 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".