Bank Competition Using Networks: A Study on an Emerging Economy
Bibliographic record
Abstract
Interconnectedness among banks is a key distinguishing feature of the banking system. It helps mitigate liquidity problems but on the other hand, acts as a curse in propagating systemic risk at times of distress. Thus, as banks cannot function in isolation, this study uses the Contemporary Theory of Networks to examine banking competition in India for five distinct economic phases, emphasizing upon the Global Financial Crisis (GFC) and the ongoing COVID-19 pandemic. This paper proposes a Market Power Network Index (MPNI), which uses network parameters to measure banks’ market power. This network structure shows a formation of bank clusters that are involved in competition. Specifically, network properties, such as centroid, average path length, the distance of a node from the centroid, the total number of connections in the inter-bank market, and network density, do go on to explain banking competition. It is interesting to note that crisis periods witness a lower level of competition, with GFC bearing the least competition. The ongoing COVID-19 pandemic shows a lower trend, but it is of a higher magnitude than GFC. It was also found that big-sized, profitable, capital adequate, and public banks dominate the banking system. Notably, this study was conducted on a sample of 33 listed Indian banks from April 2008 to December 2020.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".