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When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions

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  • Groß-Klußmann, Axel
  • Hautsch, Nikolaus

Abstract

We examine high-frequency market reactions to an intraday stock-specific news flow. Using unique pre-processed data from an automated news analytics tool based on linguistic pattern recognition we exploit information on the indicated relevance, novelty and direction of company-specific news. Employing a high-frequency VAR model based on 20 s data of a cross-section of stocks traded at the London Stock Exchange we find distinct responses in returns, volatility, trading volumes and bid-ask spreads due to news arrivals. We show that a classification of news according to indicated relevance is crucial to filter out noise and to identify significant effects. Moreover, sentiment indicators have predictability for future price trends though the profitability of news-implied trading is deteriorated by increased bid-ask spreads.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Empirical Finance.

Volume (Year): 18 (2011)
Issue (Month): 2 (March)
Pages: 321-340

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Handle: RePEc:eee:empfin:v:18:y:2011:i:2:p:321-340

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Web page: http://www.elsevier.com/locate/jempfin

Related research

Keywords: Firm-specific news News sentiment High-frequency data Volatility Liquidity Abnormal returns;

References

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  1. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
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Citations

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Cited by:
  1. Riordan, Ryan & Storkenmaier, Andreas & Wagener, Martin & Sarah Zhang, S., 2013. "Public information arrival: Price discovery and liquidity in electronic limit order markets," Journal of Banking & Finance, Elsevier, vol. 37(4), pages 1148-1159.
  2. David E. Allen & Michael McAleer & Abhay K. Singh, 2014. "Machine news and volatility: The Dow Jones Industrial Average and the TRNA sentiment series," Working Papers in Economics 14/04, University of Canterbury, Department of Economics and Finance.
  3. Andreas Storkenmaier & Martin Wagener & Christof Weinhardt, 2012. "Public information in fragmented markets," Financial Markets and Portfolio Management, Springer, vol. 26(2), pages 179-215, June.
  4. Kohonen, Anssi, 2013. "On detection of volatility spillovers in overlapping stock markets," Journal of Empirical Finance, Elsevier, vol. 22(C), pages 140-158.
  5. Jacob Boudoukh & Ronen Feldman & Shimon Kogan & Matthew Richardson, 2013. "Which News Moves Stock Prices? A Textual Analysis," NBER Working Papers 18725, National Bureau of Economic Research, Inc.
  6. Kohonen, Anssi, 2012. "On detection of volatility spillovers in simultaneously open stock markets," MPRA Paper 37504, University Library of Munich, Germany.
  7. Marcelo Bianconi & Xiaxin Hua & Chih Ming Tan, 2013. "Determinants of Systemic Risk and Information Dissemination," Working Paper Series 67_13, The Rimini Centre for Economic Analysis.
  8. Ho, Kin-Yip & Shi, Yanlin & Zhang, Zhaoyong, 2013. "How does news sentiment impact asset volatility? Evidence from long memory and regime-switching approaches," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 436-456.
  9. Robert F. Engle & Martin Klint Hansen & Asger Lunde, 2012. "And Now, The Rest of the News: Volatility and Firm Specific News Arrival," CREATES Research Papers 2012-56, School of Economics and Management, University of Aarhus.
  10. Ammann, Manuel & Frey, Roman & Verhofen, Michael, 2012. "Do Newspaper Articles Predict Aggregate Stock Returns?," Working Papers on Finance 1204, University of St. Gallen, School of Finance.
  11. Adam Clements & Neda Todorova, 2014. "The impact of information flow and trading activity on gold and oil futures volatility," NCER Working Paper Series 102, National Centre for Econometric Research.

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