Bibliographic record
Abstract
This thesis is a collection of three essays with a common theme on the riskiness of banks.The first essay (Chapter 2) examines how banks' discretionary accounting practices affect banks' stock trading liquidity and stock price crash risk.Bank managers can use discretionary loan loss provisioning practices to obscure information on banks' performance and riskiness, thus elevating information uncertainty among investors.We find empirical evidence confirming that banks with greater opacity, as measured by the discretionary loan loss provisioning (DLLP) practice, are associated with greater stock trading illiquidity and higher stock price crash risk.The second essay (Chapter 3) examines depositors' tendency to apply financial discipline to banks that adopt discretionary accounting practices.We find that uninsured deposit growth is negatively associated with banks' DLLP.The findings suggest that depositors punish banks' discretionary accounting practices.We also find that within the non-too-big-to-fail (non-TBTF) banks, depositors react sensitively to DLLP during both the 2008 financial crisis and non-crisis periods.However, for those so-called too-big-tofail (TBTF) banks, depositors react sensitively to DLLP practices only during the non-crisis period, but not during the crisis period.In addition, banks subject to U.S. government bailout actions during the 2008-2009 crisis period received less depositor discipline than other banks.The third essay (Chapter 4) examines whether tail risk for respective real (i.e., nonfinancial) sub-sectors appears to be affected by risk emanating from the financial sector.We use the Conditional Value-at-Risk ("CoVaR") approach to examine which real subsectors are more vulnerable to risk emanating from the financial sector by measuring the
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".