Boards, CEOs and bank behavior: regulatory and performance perspectives
Bibliographic record
Abstract
This thesis consists of three essays on the performance implications of senior decision-makers in the banking industry. While the first chapter looks at one aspect of bank performance from a regulatory perspective, the next two chapters study performance from an investor perspective. The first chapter uses regulatory enforcement actions issued against US banks to show that both board monitoring and advising are effective in preventing misconduct by banks. While better monitoring by boards prevents all categories of misconduct, better advising prevents misconduct of a technical nature. Board monitoring increases the likelihood that misconduct is detected, increases the penalties imposed on the CEO, and alleviates shareholder wealth losses following the detection of misconduct by regulators. This chapter offers novel insights on how to structure bank boards to prevent bank misconduct. The second chapter seeks to understand how the characteristics of bank executives affect the market performance of US banks. To explore the expected performance effects linked to executive characteristics, the changes in the market valuation of banks linked to announcements of executive appointments are estimated. The chapter shows that age, education and the prior work experience of executives create shareholder wealth while gender is not linked to measureable value effects. Furthermore, these wealth effects are moderated by the level of influence of incoming executives, with their magnitude diminished under independent boards and higher if the incoming executive is also appointed as CEO. The results are robust to the treatment of selection bias. This chapter contributes to the current debate on whether and how individual executives matter for firm performance. The findings also shed light on the value of human capital in the banking industry. The third chapter explores how the cultural heritage of senior decision-makers affects bank outcomes. To study cultural heritage, this chapter focuses on US-born CEOs who are the children or grandchildren of immigrants. Using a hand-collected dataset that tracks the family tree of US bank CEOs, it is shown that the cultural characteristics prevailing in the country of a CEO’s ancestors influence firm performance under pressure. How CEOs respond to competitive pressure is driven by specific cultural dimensions and is causally related to corporate policy choices. To establish causality, I use variation in industry competition generated by a quasi-natural experiment, the staggered adoption of barriers to US interstate branching in the 1990s. I also use an out-of-sample test using a non-banking competitive shock, the Canada-United States Free Trade Agreement, and find robust results.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| 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".