A comprehensive analysis of the decline in the market-to-book ratio of European banks
Bibliographic record
Abstract
We analyse a sample of 215 European banks and find that their market-to-book ratio has declined substantially since the GFC. To assess what may account for this, bank-specific and country-specific indicators are used, as well as, for the first time for European banks, ESG variables. We find bank fundamentals, such as ROE and volatility of stock returns to be important determinants of the market-to-book ratio. We also find the valuation of large banks to be penalized more, relative to that of smaller ones. On the country-specific front, we find GDP growth to be significant, as well as the relative size of the banking sector. As far as ESG is concerned, we find different ESG sub-pillars to affect bank valuation differently, more specifically, we find a positive relationship between duality and valuation, particularly for large banks, and a negative one for environmental engagement, the latter being suggestive of the ‘over-investment’ hypothesis. • The market-to-book ratio of European banks has been persistently declining since the Global Financial Crisis. • Return on equity and volatility of stock returns are important determinants of bank valuation, as per the standard theoretical dividend discount model. • The valuation of large banks has been penalized more, relative to that of smaller ones. • ESG sub-pillars affect bank valuation differently. • There are differences in the factors affecting large and smaller bank valuations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".