Cohesiveness in financial news and its relation to market volatility

Scientific Reports

Volumn 4, Issue , 2014, Pages

  Piškorec, Matija ^a   Antulov Fantulin, Nino ^a   Novak, Petra Kralj ^b   Mozetiä, Igor ^b   Grär, Miha ^b   Vodenska, Irena ^c   Šmuc, Tomislav ^a  

^a Laboratory for Information Systems Division of Electronics Rud er Bosikovi Institute   (Croatia)

^b Department of Knowledge Technologies Jozief Stefan Institute   (Slovenia)

^c Vose Hall 308   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84901338487     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep05038     Document Type: Article

References (28)

1. Vespignani, A. Predicting the Behavior of Techno-Social Systems. Science 325, 425-428 (2009).
2. Mitchell, T. M. Mining Our Reality. Science 326, 1644-1645 (2009).
3. Gonzalez, M. C., Hidalgo, C. A. & Barabasi, A. -L. Understanding individual human mobility patterns. Nature 453, 779-782 (2008). (Pubitemid 351793783)
4. Ginsberg, J. et al. Detecting influenza epidemics using search engine query data. Nature 457, 1012-1014 (2009).
5. Goel, S., Hofman, J. M., Lahaie, S., Pennock, D. M. & Watts, D. J. Predicting consumer behavior with web search. PNAS 107, 17486-17490 (2010).
6. Ruiz, E. J., Hristidis, V., Castillo, C., Gionis, A. & Jaimes, A. Correlating financial time series with micro-blogging activity. In Proceedings of the Fifth ACM International Conference on Web Search and Data Mining (Seattle, Washington, February 2012), WSDM' 12, 513-522 (ACM, 2012).
7. Bordino, I. et al. Web search queries can predict stock market volumes. PLoS ONE 7, e40014 (2012).
8. Dimpfl, T. & Jank, S. Can internet search queries help to predict stock market volatility? Paper presented at Finance Meeting EUROFIDAI-AFFI, Paris (2012).Available at http://ssrn. com/abstract51941680. Accessed December 21, 2013.
9. Chauvet, M., Gabriel, S. A. & Lutz, C. Fear and loathing in the housing market: Evidence from search query data (2013). Available at http://ssrn. com/ abstract52148769. Accessed January 14, 2014.
10. Preis, T., Moat, H. & Stanley, E. Quantifying trading behavior in financial markets using Google trends. Sci. Rep. 3, 1684; DOI:10. 1038/srep01684 (2013).
11. Preis, T., Moat, H. S., Stanley, H. E. & Bishop, S. R. Quantifying the advantage of looking forward. Sci. Rep. 2, 350; DOI:10. 1038/srep00350 (2012).
12. Moat, H. S. et al. Quantifying Wikipedia usage patterns before stock market moves. Sci. Rep. 3, 1801; DOI:10. 1038/srep01801 (2013).
13. Andersen, T. G., Bollerslev, T., Diebold, F. & Vega, C. Real-time price discovery in stock, bond and foreign exchange markets. J. Int. Econ. 73, 251-277 (2007). (Pubitemid 350028455)
14. Zhang, W. & Skiena, S. Trading strategies to exploit news sentiment. In Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media (Washington, D. C., May 23-26, 2010), 375-378 (The AAAI Press, 2010).
15. Chen, H., De, P., Hu, Y. J. & Hwang, B. -H. Wisdom of crowds: The value of stock opinions transmitted through social media. Rev. Financ. Stud. (2013). Available at http://ssrn. com/abstract51807265. Accessed January 14, 2014.
16. Mao, H., Counts, S. & Bollen, J. Predicting financial markets: Comparing survey, news, twitter and search engine data. arXiv:1112. 1051 (2011).
17. Casarin, R. & Squazzioni, F. Being on the field when the game is still under way: The financial press and stock markets in times of crisis. PLoS ONE 8, e67721 (2013).
18. Battiston, S., Puliga, M., Kaushik, R., Tasca, P. & Caldarelli, G. DebtRank: Too Central to Fail? Financial Networks, the FED and Systemic Risk. Sci. Rep. 2, 541; DOI:10. 1038/srep00541 (2012).
19. Huang, X., Vodenska, I., Havlin, S. & Stanley, H. E. Cascading Failures in Bipartite Graphs: Model for Systemic Risk Propagation. Sci. Rep. 3, 1219; DOI:10. 1038/srep01219 (2013).
20. Quax, R., Kandhai, D. & Sloot, P. M. A. Information dissipation as an earlywarning signal for the Lehman Brothers collapse in financial time series. Sci. Rep. 3, 1898; DOI:10. 1038/srep01898 (2013).
21. Harmon, D. et al. Predicting economic market crises using measures of collective panic. (2011). Available at http://ssrn. com/fabstract51829224. Accessed January 14, 2014.
22. Kenett, D. Y. et al. Index cohesive force analysis reveals that theUSmarket became prone to systemic collapses since 2002. PLoS ONE 6, e19378 (2011).
23. Zheng, Z., Podobnik, B., Feng, L. & Baowen, L. Change is cross-correlations as an indicator for systemic risk. Sci. Rep. 2, 888; DOI:10. 1038/srep00888 (2012).
24. Vodenska, I. & Chambers, W. J. Understanding the relationship between VIX and the S&P 500 index volatility. Paper presented at 26th Australasian Finance and Banking Conference 2013 (Sydney, Australia, December 17-19 2013). Available at http://ssrn. com/abstract52311964. Accessed January 18, 2014.
25. Da, Z., Engelbergz, J. & Gaox, P. The sum of all fears - investor sentiment and asset prices (2011).Available at http://rady. ucsd. edu/faculty/directory/engelberg/pub/ portfolios/FEARS. pdf. Accessed January 14, 2014. 27. Kaufman, G. Banking and currency crisis and systemic risk: A taxonomy and review. Finan. Markets, Inst. Instruments 9, 69 (2000).
26. Neely, C. Using implied volatility to measure uncertainty about interest rates. Fed. Reserve Bank St. Louis Rev. 87, 407-425 (2005). (Pubitemid 43678962)
27. Toda, H. Y. & Yamamoto, T. Statistical inference in vector autoregression with possibly integrated processes. J. Econometrics 66, 225-250 (1995).
28. Lo, A. Hedge funds, systemic risk, and the financial crisis of 2007-2008: Written testimony to the U. S. House of Representatives Committee on Oversight and Government Reform. (2008). ailable at http://ssrn. com/abstract51301217. Accessed January 13, 2014.