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http://hdl.handle.net/10419/39334
  
Title:Quantifying high-frequency market reactions to real-time news sentiment announcements PDF Logo
Authors:Groß-Klußmann, Axel
Hautsch, Nikolaus
Issue Date:2009
Series/Report no.:SFB 649 discussion paper 2009,063
Abstract: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.
Subjects:firm-specific news
news sentiment
high-frequency data
volatility
liquidity
abnormal returns
JEL:G14
C32
Document Type:Working Paper
Appears in Collections:SFB 649 Discussion Papers, HU Berlin

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