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Record W4409301011 · doi:10.1016/j.iref.2025.104107

A comprehensive analysis of the decline in the market-to-book ratio of European banks

2025· article· en· W4409301011 on OpenAlexaff
Stelios Markoulis, Spiros H. Martzoukos, S. Savvas, Vasilios Zagkreos

Bibliographic record

VenueInternational Review of Economics & Finance · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsPricewaterhouseCoopers (Canada)
Fundersnot available
KeywordsEconomicsEconomic history

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.395

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.266
Teacher spread0.245 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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