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Record W4416087127 · doi:10.3390/jrfm18110631

Between Stability and Exposure: The Dual Effects of Income Diversification on Bank Risk

2025· article· en· W4416087127 on OpenAlexvenueno aff
Aysa Siddika, Abdullah Sarwar, Mohammad Ali Tareq, Pallabi Siddiqua

Bibliographic record

VenueJournal of risk and financial management · 2025
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)Market liquidityRobustness (evolution)Panel dataInsolvencyFinancial stabilityFinancial intermediaryCapital requirement

Abstract

fetched live from OpenAlex

The traditional role of banks as intermediaries has been transferred to a vast array of businesses, creating many sources of income. The present study examines the impact of income diversification on bank risk. A total of 565 commercial banks from 50 countries were examined. A dynamic panel data analysis using Maximum Likelihood with Structural Equation Modelling was performed. The study found that income diversification has no significant effect on risk-weighted assets, while it reduces the insolvency risk and liquidity risk of the bank. Multiple proxies were utilized to measure bank risk to increase the robustness of the study. The study stressed the importance of income diversification and efficient capital allocation across various investment projects to survive in a highly competitive environment. Overall, this study provides new insights into the contradictory relationship between income diversification and bank risk in the global context. This would assist in developing strategies and policies to reduce risk and increase stability in the banking sector.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.010
GPT teacher head0.204
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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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