IDEAS home Printed from https://ideas.repec.org/p/arx/papers/1402.3483.html
   My bibliography  Save this paper

News Cohesiveness: an Indicator of Systemic Risk in Financial Markets

Author

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
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/1402.3483
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Pavel Ciaian & Miroslava Rajcaniova & d’Artis Kancs, 2016. "The economics of BitCoin price formation," Applied Economics, Taylor & Francis Journals, vol. 48(19), pages 1799-1815, April.
    2. Bouoiyour, Jamal & Selmi, Refk, 2014. "What Bitcoin Looks Like?," MPRA Paper 58091, University Library of Munich, Germany.
    3. Bouoiyour, Jamal & Selmi, Refk, 2014. "What Does Crypto-currency Look Like? Gaining Insight into Bitcoin Phenomenon," MPRA Paper 57907, University Library of Munich, Germany.
    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. 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.
    6. repec:pra:mprapa:58133 is not listed on IDEAS
    7. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Halousková, Martina & Stašek, Daniel & Horváth, Matúš, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Finance Research Letters, Elsevier, vol. 50(C).
    2. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2014. "Coupling news sentiment with web browsing data improves prediction of intra-day price dynamics," Papers 1412.3948, arXiv.org, revised Dec 2015.
    3. Coble, David & Pincheira, Pablo, 2017. "Nowcasting Building Permits with Google Trends," MPRA Paper 76514, University Library of Munich, Germany.
    4. Lyócsa, Štefan & Halousková, Martina & Haugom, Erik, 2023. "The US banking crisis in 2023: Intraday attention and price variation of banks at risk," Finance Research Letters, Elsevier, vol. 57(C).
    5. Martina Halouskov'a & Daniel Stav{s}ek & Mat'uv{s} Horv'ath, 2022. "The role of investor attention in global asset price variation during the invasion of Ukraine," Papers 2205.05985, arXiv.org, revised Aug 2022.
    6. Latoeiro, Pedro & Ramos, Sofía B. & Veiga, Helena, 2013. "Predictability of stock market activity using Google search queries," DES - Working Papers. Statistics and Econometrics. WS ws130605, Universidad Carlos III de Madrid. Departamento de Estadística.
    7. Semen Son-Turan, 2016. "The Impact of Investor Sentiment on the "Leverage Effect"," International Econometric Review (IER), Econometric Research Association, vol. 8(1), pages 4-18, April.
    8. Ding Ding & Chong Guan & Calvin M. L. Chan & Wenting Liu, 2020. "Building stock market resilience through digital transformation: using Google trends to analyze the impact of COVID-19 pandemic," Frontiers of Business Research in China, Springer, vol. 14(1), pages 1-21, December.
    9. Semen Son Turan, 2014. "Internet Search Volume and Stock Return Volatility: The Case of Turkish Companies," Information Management and Business Review, AMH International, vol. 6(6), pages 317-328.
    10. Gabriele Ranco & Ilaria Bordino & Giacomo Bormetti & Guido Caldarelli & Fabrizio Lillo & Michele Treccani, 2016. "Coupling News Sentiment with Web Browsing Data Improves Prediction of Intra-Day Price Dynamics," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-14, January.
    11. Audrino, Francesco & Sigrist, Fabio & Ballinari, Daniele, 2020. "The impact of sentiment and attention measures on stock market volatility," International Journal of Forecasting, Elsevier, vol. 36(2), pages 334-357.
    12. Papadamou, Stephanos & Fassas, Athanasios P. & Kenourgios, Dimitris & Dimitriou, Dimitrios, 2023. "Effects of the first wave of COVID-19 pandemic on implied stock market volatility: International evidence using a google trend measure," The Journal of Economic Asymmetries, Elsevier, vol. 28(C).
    13. Hailiang Huang & Yanhong Li & Yingying Zhang, 2018. "Investors’ attention and overpricing of IPO: an empirical study on China’s growth enterprise market," Information Systems and e-Business Management, Springer, vol. 16(4), pages 761-774, November.
    14. Gabriele Ranco & Darko Aleksovski & Guido Caldarelli & Miha Grčar & Igor Mozetič, 2015. "The Effects of Twitter Sentiment on Stock Price Returns," PLOS ONE, Public Library of Science, vol. 10(9), pages 1-21, September.
    15. David Garcia & Claudio Juan Tessone & Pavlin Mavrodiev & Nicolas Perony, 2014. "The digital traces of bubbles: feedback cycles between socio-economic signals in the Bitcoin economy," Papers 1408.1494, arXiv.org.
    16. Guo, Jian-Feng & Ji, Qiang, 2013. "How does market concern derived from the Internet affect oil prices?," Applied Energy, Elsevier, vol. 112(C), pages 1536-1543.
    17. Liu, Jian-Guo & Yang, Zhen-Hua & Li, Sheng-Nan & Yu, Chang-Rui, 2018. "A generative model for the collective attention of the Chinese stock market investors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 1175-1182.
    18. Eli Arditi & Eldad Yechiam & Gal Zahavi, 2015. "Association between Stock Market Gains and Losses and Google Searches," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-12, October.
    19. Qadan, Mahmoud & Nama, Hazar, 2018. "Investor sentiment and the price of oil," Energy Economics, Elsevier, vol. 69(C), pages 42-58.
    20. Pham, Linh & Cepni, Oguzhan, 2022. "Extreme directional spillovers between investor attention and green bond markets," International Review of Economics & Finance, Elsevier, vol. 80(C), pages 186-210.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:arx:papers:1402.3483. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.