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Principles of Mortgage Loan Portfolio Formation in Commercial Banking

2025· article· W4416207526 on OpenAlexaffabout
Junan Zhang

Bibliographic record

VenueUniversal Library of business and economics. · 2025
Typearticle
Language
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsMorgan Solar (Canada)
Fundersnot available
KeywordsUnderwritingSecuritizationMortgage underwritingPortfolioDebtLoanDiversification (marketing strategy)Mortgage insuranceShared appreciation mortgage

Abstract

fetched live from OpenAlex

This article is dedicated to the principles of forming a commercial bank’s mortgage loan portfolio, drawing on Canadian practices and comparing them with international approaches. The relevance of the topic is driven by the high share of mortgage assets on bank balance sheets and the increased debt burden of households. The novelty is formulated by linking regulatory requirements, insurance mechanisms, diversification, and asset-liability matching into a unified risk management framework. The paper describes underwriting standards, the logic of mandatory and portfolio insurance, diversification strategies that account for climate factors, and the impact of securitization on liquidity and capital. The study examines regulatory documents, industry reports, and academic publications on credit risk and stress testing. Particular attention is given to the consequences of short-term interest rate fixation in Canada and the impact of term renewals on portfolio stability. The work aims to develop a set of principles for maintaining an acceptable risk-return trade-off. To achieve this objective, comparative analysis, synthesis, regulatory analysis, and elements of scenario-based stress testing were employed. The conclusion describes the practical applicability of the findings for banks and supervisory authorities. The article will be useful for risk managers, regulators, and researchers of banking stability.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.527
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
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 head0.017
GPT teacher head0.191
Teacher spread0.174 · 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.

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 routes2
Has abstractyes

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