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Record W2095867786 · doi:10.1002/9781118445785.ch1

Foreign Exchange Market Structure, Players, and Evolution

2012· other· en· W2095867786 on OpenAlexaff
Michael R. King, Carol L. Osler, Dagfinn Rime

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsWestern University
Fundersnot available
KeywordsElectronic tradingForeign exchange marketOpen outcryAlgorithmic tradingAlternative trading systemStylized factMarket microstructureCurrencyBusinessCapital marketExchange rateDark liquidityIncentiveMonetary economicsHigh-frequency tradingEconomicsFinancial economicsCommerceOrder (exchange)Market economyFinanceMacroeconomics

Abstract

fetched live from OpenAlex

Exchange rates affect output and employment, inflation through the cost of imports and commodity prices and international capital flows through the risks and returns of different assets. This chapter describes the foreign exchange (FX) market and presents new evidence on recent trends, thereby setting the stage for the rest of the handbook. It presents stylized facts on the market’s size and composition. The chapter looks at the motives, incentives, and constraints of the major players and describes the momentous changes in trading practices and market structure that have taken place over the recent decades. It describes market transformation caused by the electronic trading revolution. To better understand FX activity on multibank trading systems and electronic brokers, the results of a survey of 15 institutional and retail platforms is presented. The chapter also examines the state of play in the global FX market, which reflects both stability and rapid technological change. Controlled Vocabulary Terms currency trading; electronic trading; foreign exchange markets; foreign exchange rate; multibank trading platforms; trading

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.442
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0500.000

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 head0.019
GPT teacher head0.191
Teacher spread0.171 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations83
Published2012
Admission routes1
Has abstractyes

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