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

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 
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
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Metadaten
Author:Axel Groß-Klußmann, Nikolaus Hautsch
URN:urn:nbn:de:hebis:30-73618
Series (Serial Number):CFS working paper series (2009, 31)
Document Type:Working Paper
Language:English
Date of Publication (online):2010/01/13
Year of first Publication:2009
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2010/01/13
Tag:Abnormal Returns; Firm-specific News ; High-frequency Data ; Liquidity ; News Sentiment ; Volatility
HeBIS PPN:220677433
Institutes:Center for Financial Studies (CFS)
Dewey Decimal Classification:330 Wirtschaft
Sammlungen:Universitätspublikationen
Licence (German):License Logo Veröffentlichungsvertrag für Publikationen

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