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Record W2041745364 · doi:10.1080/13518470801892236

Trading time and trading activity: evidence from extensions of the NYSE trading day

2008· article· en· W2041745364 on OpenAlexaff
Ebenezer Asem, Aditya Kaul

Bibliographic record

VenueEuropean Journal of Finance · 2008
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Markets and Investment Strategies
Canadian institutionsUniversity of AlbertaUniversity of Lethbridge
Fundersnot available
KeywordsExtension (predicate logic)Volume (thermodynamics)Stock (firearms)Stock exchangePrice discoveryFinancial economicsEconomicsMonetary economicsEconometricsComputer scienceFinanceFutures contractHistory

Abstract

fetched live from OpenAlex

The New York Stock Exchange extended its trading hours by 30 min in 1974 and in 1985; the first extension resulting in a delayed close and the second in an early open. We find a shift in volume to the new period after each extension. Additionally, there is a larger increase in volume after the 1985 extension than after the 1974 extension. We argue that the second effect is explained by the first. The extension at the end of the day allows some investors to postpone their trades, which results in occasional information cancellation or discovery; this mutes the effect of the extension on volume. In contrast, the extension at the start of the day allows some investors to accelerate trades, which precludes information cancellation or discovery and its negative effect on volume. This explanation suggests that the effect of an extension on volume depends, at least in part, on its timing.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.072
GPT teacher head0.213
Teacher spread0.141 · 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 source (direct Gemma or distilled Codex), 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

Citations3
Published2008
Admission routes1
Has abstractyes

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