The Financial Crisis of Banks (Before, During and After): An Intellectual Capital Perspective
Bibliographic record
Abstract
The purpose of this paper is to evaluate the link between intellectual capital components and financial performance across three temporal periods on either side of a financial crisis. The study offers a longitudinal approach, combining two data collecting methods. A survey on intellectual capital components was administered during the initial period followed by objective performance ratios in subsequent time periods (covering pre, during and post‐financial crisis analysis). Regarding the three periods in the study, evidence seems to support the argument that intellectual capital scores are good predictors of future banking performance. One bank in particular that was ranked very low in 2005 intellectual capital scores eventually failed to survive autonomously. By 2012, it was forced to be rescued by governmental aid using public funds. Generally speaking, we can argue that intellectual capital average scores are good predictors of future banking performance. The study's generalizability is limited to the Portuguese banking industry. This is the first academic research study to evaluate the link between intellectual capital and the financial performance of banks across three temporal periods: 2005–2006 (pre‐crisis), 2007–2008 (during crisis), and 2009–10 (post‐crisis). Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".