TY  - UNPD
A1  - Groß-Klußmann, Axel
A1  - Hautsch, Nikolaus
T1  - Quantifying high-frequency market reactions to real-time news sentiment announcements
T2  - Center for Financial Studies (Frankfurt am Main): CFS working paper series ; No. 2009,31
N2  - 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. JEL-Classification: G14, C32
T3  - CFS working paper series - 2009, 31 
KW  - Firm-specific News
KW  - News Sentiment
KW  - High-frequency Data
KW  - Volatility
KW  - Liquidity
KW  - Abnormal Returns
KW  - Tagesgeschäft
KW  - Volatilität
KW  - Value at Risk
Y1  - 2009
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/7389
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30-73618
ER  -