Latest documents http://publikationen.ub.uni-frankfurt.de/index/index/ Wed, 13 Jan 2010 15:52:54 +0100 Wed, 13 Jan 2010 15:52:54 +0100 http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7389 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 JEL_Classification: G14, C32 Axel Groß-Klußmann; Nikolaus Hautsch workingpaper http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7389 Wed, 13 Jan 2010 15:52:54 +0100