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Record W2163689154 · doi:10.1287/inte.33.2.12.14465

Applying Operations Research Techniques to Financial Markets

2003· article· en· W2163689154 on OpenAlex

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

VenueINFORMS Journal on Applied Analytics · 2003
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Portfolio Optimization
Canadian institutionsUniversity of British Columbia
FundersUniversity of NottinghamLondon School of Economics and Political Science
KeywordsFinancial marketEquity (law)DebtFinanceBusinessFinancial modelingMarket dataFinancial engineeringEconomics

Abstract

fetched live from OpenAlex

OR techniques are applied to nonportfolio problems in financial markets, such as the equity, debt, and foreign exchange markets and the corresponding derivatives markets. Finance problems are an excellent application area for OR researchers. OR techniques are used to value financial instruments, identify market imperfections, design securities, regulate markets, evaluate and control risks, model strategic problems, and understand the functioning of financial markets. Mathematical programming is probably the most widely applied OR technique, but Monte Carlo simulation methods are of increasing importance. With the improvements in the real-time availability of data and the power of computers, the role of OR techniques in financial markets can only increase.

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)0.009
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.131
GPT teacher head0.430
Teacher spread0.299 · 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