Abstract
In this paper, we combine network analytical methods to understand the structure of financial markets with recent research about collective attention shifts by utilizing massive social media data. Our main goal, hence, is to investigate whether changes in stock networks are connected with collective attention shifts. To examine the relationship between structural market properties and mass online behavior empirically, we merge company-level Google Trends data with stock network dynamics for all S&P 100 corporations between 2004 and 2014. The interplay of massive online behavior and market activities reveals that collective attention shifts precede structural changes in stock market networks and that this connection is mostly carried by companies that already dominate the development of the S&P 100.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Yakovenko, V.M., Rosser Jr., J.B.: Colloquium: Statistical mechanics of money, wealth, and income. Rev. Mod. Phys. 81, 1703 (2009)
Mantegna, R.N.: Hierarchical structure in financial markets. Eur. Phys. J B-Condens. Matter Complex Syst. 11, 193–197 (1999)
Bonanno, G., Caldarelli, G., Lillo, F., Mantegna, R.N.: Topology of correlation-based minimal spanning trees in real and model markets. Phys. Rev. E. 68, 046130 (2003)
Dias, J.: Sovereign debt crisis in the European Union: A minimum spanning tree approach. Phys. Stat. Mech. Its Appl. 391, 2046–2055 (2012)
Gilmore, C.G., Lucey, B.M., Boscia, M.W.: Comovements in government bond markets: A minimum spanning tree analysis. Phys. Stat. Mech. Its Appl. 389, 4875–4886 (2010)
Onnela, J.-P., Chakraborti, A., Kaski, K., Kertész, J.: Dynamic asset trees and Black Monday. Phys. Stat. Mech. Its Appl. 324, 247–252 (2003)
Onnela, J.-P., Chakraborti, A., Kaski, K., Kertesz, J., Kanto, A.: Dynamics of market correlations: Taxonomy and portfolio analysis. Phys. Rev. E. 68, 056110 (2003)
Onnela, J.-P., Chakraborti, A., Kaski, K., Kertesz, J., Kanto, A.: Asset trees and asset graphs in financial markets. Phys. Scr. T106, 48 (2003)
Tse, C.K., Liu, J., Lau, F.: A network perspective of the stock market. J. Empir. Finance 17, 659–667 (2010)
Liu, J., Tse, C.K., He, K.: Fierce stock market fluctuation disrupts scalefree distribution. Quant. Finance 11, 817–823 (2011)
Heiberger, R.H.: Stock network stability in times of crisis. Phys. Stat. Mech. Its Appl. 393, 376–381 (2014)
Tumminello, M., Lillo, F., Mantegna, R.N.: Correlation, hierarchies, and networks in financial markets. J. Econ. Behav. Organ. 75, 40–58 (2010)
Mathiesen, J., Angheluta, L., Ahlgren, P.T.H., Jensen, M.H.: Excitable human dynamics driven by extrinsic events in massive communities. Proc. Natl. Acad. Sci. 110, 17259–17262 (2013)
Bordino, I., Battiston, S., Caldarelli, G., Cristelli, M., Ukkonen, A., Weber, I.: Web Search Queries Can Predict Stock Market Volumes. PLoS ONE 7, e40014 (2012)
Moat, H.S., Curme, C., Avakian, A., Kenett, D.Y., Stanley, H.E., Preis, T.: Quantifying Wikipedia Usage Patterns Before Stock Market Moves. Sci. Rep. 3 (2013)
Bollen, J., Mao, H., Zeng, X.: Twitter mood predicts the stock market. J. Comput. Sci. 2, 1–8 (2011)
Preis, T., Reith, D., Stanley, H.E.: Complex dynamics of our economic life on different scales: insights from search engine query data. Philos. Trans. R. Soc. Lond. Math. Phys. Eng. Sci. 368, 5707–5719 (2010)
Kristoufek, L.: Can Google Trends search queries contribute to risk diversification? Sci. Rep. 3 (2013)
Preis, T., Moat, H.S., Stanley, H.E.: Quantifying Trading Behavior in Financial Markets Using Google Trends. Sci. Rep. 3 (2013)
S&P 100 - S&P Dow Jones Indices. http://us.spindices.com/indices/equity/sp-100
Yahoo Finance. https://finance.yahoo.com/
May, R.M., Levin, S.A., Sugihara, G.: Complex systems: Ecology for bankers. Nature 451, 893–895 (2008)
May, R.M.: Will a Large Complex System be Stable? Nature 238, 413–414 (1972)
Dobson, A., Allesina, S., Lafferty, K., Pascual, M. (eds.): Food-web assembly and collapse: mathematical models and implications for conservation. Philos. Trans. R. Soc. B Biol. Sci. 364, 1643–1806 (2009)
Newman, M.E.: Modularity and Community Structure in Networks. Proc. Natl. Acad. Sci. 103, 8577–8582 (2006)
Girvan, M., Newman, M.E.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821–7826 (2002)
Blondel, V.D., Guillaume, J.-L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008, P10008 (2008)
Freeman, L.: Centrality in social networks: Conceptual clarification. Soc. Netw. 1, 215–239 (1979)
Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol. 2, 113–120 (1972)
Ahern, K.R.: Network Centrality and the Cross Section of Stock Returns. Social Science Research Network, Rochester (2013)
Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27, 55–71 (2005)
Merton, R.K.: The Matthew Effect in Science The Reward and Communication Systems of Science Are Considered. Science 159, 56–63 (1968)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Heiberger, R.H. (2015). Shifts in Collective Attention and Stock Networks. In: Thai, M., Nguyen, N., Shen, H. (eds) Computational Social Networks. CSoNet 2015. Lecture Notes in Computer Science(), vol 9197. Springer, Cham. https://doi.org/10.1007/978-3-319-21786-4_26
Download citation
DOI: https://doi.org/10.1007/978-3-319-21786-4_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-21785-7
Online ISBN: 978-3-319-21786-4
eBook Packages: Computer ScienceComputer Science (R0)