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    Non-scheduled News Arrival and High-Frequency Stock Market Dynamics: Evidence From the Australian Securities Exchange

    200614_200614.pdf (787.9Kb)
    Access Status
    Open access
    Authors
    Smales, Lee
    Date
    2014
    Type
    Journal Article
    
    Metadata
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    Citation
    Smales, L. 2014. Non-scheduled News Arrival and High-Frequency Stock Market Dynamics: Evidence From the Australian Securities Exchange. Research in International Business and Finance. 32: pp. 122-138.
    Source Title
    Research in International Business and Finance
    DOI
    10.1016/j.ribaf.2014.03.006
    ISSN
    0275-5319
    School
    School of Economics and Finance
    Remarks

    NOTICE: this is the author’s version of a work that wasaccepted for publication in the journal Research inInternational Business and Finance. Changes resulting fromthe publishing process, such as peer review, editing,corrections, structural formatting, and other quality controlmechanisms may not be reflected in this document. Changesmay have been made to this work since it was submitted forpublication. A definitive version was subsequently publishedin the journal Research in International Business and Finance,Vol.32, (2014). DOI: 10.1016/j.ribaf.2014.03.006

    URI
    http://hdl.handle.net/20.500.11937/25695
    Collection
    • Curtin Research Publications
    Abstract

    An increasing number of market participants utilise news analytics software to comprehend the large amounts of unstructured data flowing through news-wires. Utilising original data from one such tool – Ravenpack – I examine the market reaction of leading Australian stocks to stock-specific news flow over an extended period. Unconditional analysis of key variables around 484,440 news items reveals distinct responses in market activity, volatility, bid-ask spreads and returns. The study confirms previous literature such that indicated relevance of news items is critical when identifying significant effects. In addition, the reaction of market activity, volatility and spreads is greatest for negative news. The findings are confirmed when controlling for market dynamics and cross-dependencies between variables in a high-frequency VAR model.

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