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News Cohesiveness: an Indicator of Systemic Risk in Financial Markets

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Listed:
  • Matija Piv{s}korec
  • Nino Antulov-Fantulin
  • Petra Kralj Novak
  • Igor Mozetiv{c}
  • Miha Grv{c}ar
  • Irena Vodenska
  • Tomislav v{S}muc

Abstract

Motivated by recent financial crises significant research efforts have been put into studying contagion effects and herding behaviour in financial markets. Much less has been said about influence of financial news on financial markets. We propose a novel measure of collective behaviour in financial news on the Web, News Cohesiveness Index (NCI), and show that it can be used as a systemic risk indicator. We evaluate the NCI on financial documents from large Web news sources on a daily basis from October 2011 to July 2013 and analyse the interplay between financial markets and financially related news. We hypothesized that strong cohesion in financial news reflects movements in the financial markets. Cohesiveness is more general and robust measure of systemic risk expressed in news, than measures based on simple occurrences of specific terms. Our results indicate that cohesiveness in the financial news is highly correlated with and driven by volatility on the financial markets.

Suggested Citation

  • Matija Piv{s}korec & Nino Antulov-Fantulin & Petra Kralj Novak & Igor Mozetiv{c} & Miha Grv{c}ar & Irena Vodenska & Tomislav v{S}muc, 2014. "News Cohesiveness: an Indicator of Systemic Risk in Financial Markets," Papers 1402.3483, arXiv.org.
  • Handle: RePEc:arx:papers:1402.3483
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    References listed on IDEAS

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    1. Xuqing Huang & Irena Vodenska & Shlomo Havlin & H. Eugene Stanley, 2012. "Cascading Failures in Bi-partite Graphs: Model for Systemic Risk Propagation," Papers 1210.4973, arXiv.org, revised Jan 2013.
    2. Huina Mao & Scott Counts & Johan Bollen, 2011. "Predicting Financial Markets: Comparing Survey, News, Twitter and Search Engine Data," Papers 1112.1051, arXiv.org.
    3. Ilaria Bordino & Stefano Battiston & Guido Caldarelli & Matthieu Cristelli & Antti Ukkonen & Ingmar Weber, 2012. "Web Search Queries Can Predict Stock Market Volumes," PLOS ONE, Public Library of Science, vol. 7(7), pages 1-17, July.
    4. Toda, Hiro Y. & Yamamoto, Taku, 1995. "Statistical inference in vector autoregressions with possibly integrated processes," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 225-250.
    5. Thomas Dimpfl & Stephan Jank, 2016. "Can Internet Search Queries Help to Predict Stock Market Volatility?," European Financial Management, European Financial Management Association, vol. 22(2), pages 171-192, March.
    6. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Vega, Clara, 2007. "Real-time price discovery in global stock, bond and foreign exchange markets," Journal of International Economics, Elsevier, vol. 73(2), pages 251-277, November.
    7. Dion Harmon & Marcus A. M. de Aguiar & David D. Chinellato & Dan Braha & Irving R. Epstein & Yaneer Bar-Yam, 2011. "Predicting economic market crises using measures of collective panic," Papers 1102.2620, arXiv.org.
    8. Roberto Casarin & Flaminio Squazzoni, 2013. "Being on the Field When the Game Is Still Under Way. The Financial Press and Stock Markets in Times of Crisis," PLOS ONE, Public Library of Science, vol. 8(7), pages 1-14, July.
    9. Roberto Casarin & Flaminio Squazzoni, 2012. "Financial press and stock markets in times of crisis," Working Papers 2012_04, Department of Economics, University of Venice "Ca' Foscari".
    10. Vlastakis, Nikolaos & Markellos, Raphael N., 2012. "Information demand and stock market volatility," Journal of Banking & Finance, Elsevier, vol. 36(6), pages 1808-1821.
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    Cited by:

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    2. Bouoiyour, Jamal & Selmi, Refk & Tiwari, Aviral, 2014. "Is Bitcoin business income or speculative bubble? Unconditional vs. conditional frequency domain analysis," MPRA Paper 59595, University Library of Munich, Germany.
    3. repec:pra:mprapa:58133 is not listed on IDEAS
    4. Jamal Bouoiyour & Refk Selmi, 2015. "What Does Bitcoin Look Like?," Annals of Economics and Finance, Society for AEF, vol. 16(2), pages 449-492, November.
    5. Jamal Bouoiyour & Refk Selmi & Aviral Kumar Tiwari, 2015. "Is Bitcoin Business Income Or Speculative Foolery? New Ideas Through An Improved Frequency Domain Analysis," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 10(01), pages 1-23.
    6. Bouoiyour, Jamal & Selmi, Refk, 2014. "What Bitcoin Looks Like?," MPRA Paper 58091, University Library of Munich, Germany.
    7. Bouoiyour, Jamal & Selmi, Refk, 2014. "What Does Crypto-currency Look Like? Gaining Insight into Bitcoin Phenomenon," MPRA Paper 57907, University Library of Munich, Germany.

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