When Machines Read the News: Using Automated Text Analytics to Quantify High Frequency News Impacts

Posted: 19 Jan 2010 Last revised: 3 Mar 2011

See all articles by Axel Groß-Klußmann

Axel Groß-Klußmann

Humboldt Universität zu Berlin

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research; Center for Financial Studies (CFS); Vienna Graduate School of Finance (VGSF)

Date Written: December 20, 2009

Abstract

We examine intraday market reactions to stock-specific news. Using pre-processed data from an automated news analytics tool based on linguistic pattern recognition we exploit information on the relevance and the direction of company-specific news. Concise news-implied reactions in returns, volatility as well as liquidity demand and supply are quantified by a high-frequency VAR model on 20 second intervals. We find significant responses in prices, volatility, spreads and trading volume to relevant news for a cross-section of stocks traded at the London Stock Exchange. The results confirm the usefulness of linguistic news classification in structuring the very noisy intraday news flow.

Keywords: firm-specific news, news sentiment, high-frequency data, volatility, liquidity, abnormal returns

JEL Classification: G14, C32

Suggested Citation

Groß-Klußmann, Axel and Hautsch, Nikolaus, When Machines Read the News: Using Automated Text Analytics to Quantify High Frequency News Impacts (December 20, 2009). Available at SSRN: https://ssrn.com/abstract=1536005

Axel Groß-Klußmann (Contact Author)

Humboldt Universität zu Berlin ( email )

Alexanderstr 5
Berlin, Berlin 10178
Germany

Nikolaus Hautsch

University of Vienna - Department of Statistics and Operations Research ( email )

Oskar-Morgenstern-Platz 1
Vienna, A-1090
Austria

Center for Financial Studies (CFS) ( email )

Grüneburgplatz 1
Frankfurt am Main, 60323
Germany

Vienna Graduate School of Finance (VGSF) ( email )

Welthandelsplatz 1
Vienna, 1020
Austria

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