Quantifying high-frequency market reactions to real-time news sentiment announcements
AbstractWe 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. --
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Bibliographic InfoPaper provided by Center for Financial Studies (CFS) in its series CFS Working Paper Series with number 2009/31.
Date of creation: 2009
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Firm-specific News; News Sentiment; High-frequency Data; Volatility; Liquidity; Abnormal Returns;
Other versions of this item:
- Axel Groß-Klußmann & Nikolaus Hautsch, 2009. "Quantifying High-Frequency Market Reactions to Real-Time News Sentiment Announcements," SFB 649 Discussion Papers SFB649DP2009-063, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
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