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Record W3125096218

A Microstructure Study of Circuit Breakers in the Chinese Stock Markets

2019· preprint· en· W3125096218 on OpenAlexafffund
Steven Shuye Wang, Kuan Xu, Hao Zhang

Bibliographic record

VenueRePEc: Research Papers in Economics · 2019
Typepreprint
Languageen
FieldDecision Sciences
TopicStock Market Forecasting Methods
Canadian institutionsUniversity of VictoriaSaint Mary's UniversityDalhousie University
FundersUniversity of TorontoRenmin University of ChinaNational Natural Science Foundation of China
KeywordsCircuit breakerStock marketMarket microstructureMarket depthVolatility (finance)Stock (firearms)EconomicsFinancial economicsEconometricsMonetary economicsOrder (exchange)BusinessFinanceElectrical engineeringEngineering
DOInot available

Abstract

fetched live from OpenAlex

Based on rare policy changes in the Chinese stock market in January 2016, we study the impacts of market-wide circuit breakers on market microstructure.To test if market-wide circuit breakers have the "cooling effect" and the magnet effect, we use high frequency transactions and limit order book data and Lasso IV models for endogenous market microstructure variables and exogenous policy variables based on a novel identification strategy.We find that market-wide circuit breakers have no "cooling effect" in decelerating falling prices (or returns) or reducing market volatility and order imbalance.Their presence does not affect bid-ask spreads but does reduces the large-, mid-, and small-sized trades in volume and trades.We also find that marketwide circuit breakers indeed induce significant magnet effects on stock returns, order imbalance, quote imbalance, and trades of various sizes.

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)0.045
metaresearch head score (Gemma)0.025
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Open science, Research integrity
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.393
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0450.025
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0060.003
Research integrity0.0010.004
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 head0.104
GPT teacher head0.429
Teacher spread0.325 · 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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes2
Has abstractyes

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