Quantifying high-frequency market reactions to real-time news sentiment announcements - Axel Groß-Klußmann; Nikolaus Hautsch (Goethe University Frankfurt, Center for Financial Studies (CFS), 2009)
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.
urn:nbn:de:hebis:30-73618
Keywords: G14; C32; 330; Firm-specific News; News Sentiment; High-frequency Data; Volatility; Liquidity; Abnormal Returns; Börsenkurs; Kapitalertrag; Volatilität; Ankündigungseffekt; Publizitätspflicht; Informationseffizienz; Marktliquidität; Schätzung; Großbritannien