The Effects of Bank Consolidation on Risk Capital Allocation and Market Liquidity
Bibliographic record
Abstract
This paper investigates the effects of financial market consolidation on risk capital allocation in a financial institution and the implications for market liquidity in dealership markets. We show that an increase in financial market consolidation can have ambiguous effects on liquidity in foreign exchange and government securities markets. The framework employed assumes that financial institutions use risk-management tools (for example, value-at-risk) in the allocation of risk capital. Capital is determined at the firm level and allocated among separate business lines, or divisions. The ability of market-makers to supply liquidity is influenced by their risk-bearing capacity, which is directly related to the amount of risk capital allocated to this activity. A model of inter-dealer trading is developed that is similar to the framework of Volger (1997). However, we allow for heterogeneity among dealers with respect to their risk-bearing capacity. The allocation of risk capital within financial institutions has implications for the types of mergers among financial institutions that can be beneficial for market quality. This effect depends on the correlation among cash flows from business activities that the newly merged financial institution will engage in. A negative correlation between market-making and the new activities of a merged firm suggests the possibility of increased market liquidity. Our results suggest that, when faced with a proposed merger between financial institutions, policy-makers and regulators would want to examine the correlations among division cash flows.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".