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

Axel Groß-Klußmann; Nikolaus Hautsch

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. -- firm-specific news ; news sentiment ; high-frequency data ; volatility ; liquidity ; abnormal returns

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Year of publication:	2009
Authors:	Groß-Klußmann, Axel ; Hautsch, Nikolaus
Publisher:	Berlin : SFB 649, Economic Risk
Subject:	Börsenkurs \| Share price \| Kapitaleinkommen \| Capital income \| Volatilität \| Volatility \| Ankündigungseffekt \| Announcement effect \| Unternehmenspublizität \| Corporate disclosure \| Informationseffizienz \| Informational efficiency \| Marktliquidität \| Market liquidity \| Schätzung \| Estimation \| Großbritannien \| United Kingdom