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

Axel Groß-Klußmann and Nikolaus Hautsch

No 2009-063, SFB 649 Discussion Papers from Humboldt University Berlin, Collaborative Research Center 649: Economic Risk

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.

Keywords: firm-specific news; news sentiment; high-frequency data; volatility; liquidity; abnormal returns (search for similar items in EconPapers)
JEL-codes: C32 G14 (search for similar items in EconPapers)
Date: 2009
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Working Paper: Quantifying high-frequency market reactions to real-time news sentiment announcements (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:sfb649:sfb649dp2009-063

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