Intellectual Capital and Bank Risk in Vietnam—A Quantile Regression Approach
Bibliographic record
Abstract
This study empirically presents evidence of nonlinearity and heterogeneity relation between intellectual capital and risk-taking for the Vietnamese banking system. We used quantile regression methods on a data set of 30 Vietnamese banks from 2007 to 2019. The results showed that bank insolvency was positively affected by its value-added intellectual coefficient (VAIC) at the upper quantiles (i.e., 80th and 90th), while the opposite was true for credit risk (i.e., 10th and 20th quantiles). When observing the VAIC’s components, risk-taking behaviors were also significantly affected by HCE (Human Capital Efficiency), CEE (Capital Employed Efficiency) and SCE (Structural Capital Efficiency) at the 90th quantile of instability distribution and the 10th quantile of credit risk distribution. Furthermore, the results also emphasized that there was an inverse U-shaped association between intellectual capital and bank risk-taking. Therefore, this study provides important implications for policymakers, regulators, bank managers and academics that encourage increasing investment in knowledge assets to minimize bank risks in the long run.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".