Quantifying high-frequency market reactions to real-time news sentiment announcements

  • We examine intra-day market reactions to news in stock-specific sentiment disclosures. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we extract information on the relevance as well as the direction of company-specific news. Information-implied reactions in returns, volatility as well as liquidity demand and supply are quantified by a high-frequency VAR model using 20 second intervals. Analyzing a cross-section of stocks traded at the London Stock Exchange (LSE), we find market-wide robust news-dependent responses in volatility and trading volume. However, this is only true if news items are classified as highly relevant. Liquidity supply reacts less distinctly due to a stronger influence of idiosyncratic noise. Furthermore, evidence for abnormal highfrequency returns after news in sentiments is shown. JEL-Classification: G14, C32

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Metadaten
Author:Axel Groß-Klußmann, Nikolaus HautschORCiDGND
URN:urn:nbn:de:hebis:30-73618
Parent Title (German):Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2009,31
Series (Serial Number):CFS working paper series (2009, 31)
Document Type:Working Paper
Language:English
Year of Completion:2009
Year of first Publication:2009
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2010/01/13
Tag:Abnormal Returns; Firm-specific News; High-frequency Data; Liquidity; News Sentiment; Volatility
GND Keyword:Tagesgeschäft; Volatilität; Value at Risk
HeBIS-PPN:220677433
Institutes:Wissenschaftliche Zentren und koordinierte Programme / Center for Financial Studies (CFS)
Dewey Decimal Classification:3 Sozialwissenschaften / 33 Wirtschaft / 330 Wirtschaft
Licence (German):License LogoDeutsches Urheberrecht