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Record W2991742811

Enterprise-wide asset and liability management: Issues, institutions, and models

2008· book-chapter· en· W2991742811 on OpenAlex
Dan Rosen, Stavros A. Zenios

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicRisk Management in Financial Firms
Canadian institutionsFields Institute for Research in Mathematical Sciences
Fundersnot available
KeywordsEnterprise risk managementBusinessRisk managementLiabilityAsset managementAsset (computer security)Financial managementProcess managementFinanceKnowledge managementRisk analysis (engineering)AccountingComputer scienceComputer security
DOInot available

Abstract

fetched live from OpenAlex

Publisher Summary This chapter defines enterprise risk management (ERM), describes a conceptual framework for an ERM strategy, and touches upon organizational issues. Asset and liability management (ALM) is then identified as a core activity of ERM for financial institutions. It discusses ALM for various financial institutions, and provides an overview of tools to support ALM activities. Enterprise risk management aligns a firm's business strategy with the risk factors of its environment in pursuit of business objectives. It is considered a well-grounded management strategy for corporations. The management of assets and liabilities is at the core of ERM for financial institutions. In this chapter, we discuss the general framework for ERM, and the role of ALM within this broader strategy. From the general concepts, we proceed to focus on specific financial institutions, and conclude with a discussion of modelling issues that arise in the enterprise-wide management of assets and liabilities.

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.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.757
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
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 head0.035
GPT teacher head0.229
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

Quick stats

Citations27
Published2008
Admission routes1
Has abstractyes

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