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Record W264948781

State of Information Technology Support for Traders in Fixed Income Markets

2005· article· en· W264948781 on OpenAlexafffund
Ali Reza Montazemi, John J. Siam

Bibliographic record

VenueJournal of the Association for Information Systems · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFixed incomeEquity (law)BusinessDatabase transactionFixed assetContext (archaeology)LoanFinanceEconomicsCommerceMicroeconomicsBondProduction (economics)
DOInot available

Abstract

fetched live from OpenAlex

A fixed-income security is defined as one whose income stream is fixed for the duration of the loan and where the maturity and face value are known. It is estimated that the global fixed-income market is about $40 trillion with the US having the lion’s share of $19 trillion. There were at least 74 trading platforms in North America and Europe in 2004. However, it is estimated that only about five percent of fixed-income trade is performed through electronic transaction systems. This is very low when compared with use of information systems in support of equity trade. Our research is guided by the following central question: What are the implications of using IT to mediate electronic brokerage relationships that are enacted through the work practices and interactions of actors representing buyers and sellers in financial institutions within the context of fixed income market. This paper, based on interviews with the senior managements and traders of 10 major financial institutions, provides an overview of information system support for traders in fixed-income trade markets.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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

Citations4
Published2005
Admission routes2
Has abstractyes

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