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Dynamics of investor spanning trees around dot-com bubble

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  • Sindhuja Ranganathan
  • Mikko Kivelä
  • Juho Kanniainen

Abstract

We identify temporal investor networks for Nokia stock by constructing networks from correlations between investor-specific net-volumes and analyze changes in the networks around dot-com bubble. The analysis is conducted separately for households, financial, and non-financial institutions. Our results indicate that spanning tree measures for households reflected the boom and crisis: the maximum spanning tree measures had a clear upward tendency in the bull markets when the bubble was building up, and, even more importantly, the minimum spanning tree measures pre-reacted the burst of the bubble. At the same time, we find less clear reactions in the minimal and maximal spanning trees of non-financial and financial institutions around the bubble, which suggests that household investors can have a greater herding tendency around bubbles.

Suggested Citation

  • Sindhuja Ranganathan & Mikko Kivelä & Juho Kanniainen, 2018. "Dynamics of investor spanning trees around dot-com bubble," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-14, June.
    • Handle: RePEc:plo:pone00:0198807
    DOI: 10.1371/journal.pone.0198807
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    as
    1. Fabrizio Lillo & Salvatore Miccich� & Michele Tumminello & Jyrki Piilo & Rosario N. Mantegna, 2015. "How news affects the trading behaviour of different categories of investors in a financial market," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 213-229, February.
    2. Ron Kaniel & Gideon Saar & Sheridan Titman, 2008. "Individual Investor Trading and Stock Returns," Journal of Finance, American Finance Association, vol. 63(1), pages 273-310, February.
    3. Brennan, Michael J & Cao, H Henry, 1997. "International Portfolio Investment Flows," Journal of Finance, American Finance Association, vol. 52(5), pages 1851-1880, December.
    4. R. Mantegna, 1999. "Hierarchical structure in financial markets," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 11(1), pages 193-197, September.
    5. Jaroslaw Kwapien & Pawel Oswiecimka & Marcin Forczek & Stanislaw Drozdz, 2016. "Minimum spanning tree filtering of correlations for varying time scales and size of fluctuations," Papers 1610.08416, arXiv.org, revised May 2017.
    6. Han N. Ozsoylev & Johan Walden & M. Deniz Yavuz & Recep Bildik, 2014. "Investor Networks in the Stock Market," Review of Financial Studies, Society for Financial Studies, vol. 27(5), pages 1323-1366.
    7. Kalev, Petko S. & Nguyen, Anh H. & Oh, Natalie Y., 2008. "Foreign versus local investors: Who knows more? Who makes more?," Journal of Banking & Finance, Elsevier, vol. 32(11), pages 2376-2389, November.
    8. Terrance Odean, 1998. "Are Investors Reluctant to Realize Their Losses?," Journal of Finance, American Finance Association, vol. 53(5), pages 1775-1798, October.
    9. Zhou, Wei-Xing & Sornette, Didier, 2009. "A case study of speculative financial bubbles in the South African stock market 2003–2006," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 869-880.
    10. Tse, Chi K. & Liu, Jing & Lau, Francis C.M., 2010. "A network perspective of the stock market," Journal of Empirical Finance, Elsevier, vol. 17(4), pages 659-667, September.
    11. Stanislao Gualdi & Giulio Cimini & Kevin Primicerio & Riccardo Di Clemente & Damien Challet, 2016. "Statistically validated network of portfolio overlaps and systemic risk," Post-Print hal-01705092, HAL.
    12. Jang, Wooseok & Lee, Junghoon & Chang, Woojin, 2011. "Currency crises and the evolution of foreign exchange market: Evidence from minimum spanning tree," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(4), pages 707-718.
    13. Kk{e}stutis Baltakys & Juho Kanniainen & Frank Emmert-Streib, 2017. "Multilayer Aggregation with Statistical Validation: Application to Investor Networks," Papers 1708.09850, arXiv.org, revised May 2018.
    14. Jiang, Zhi-Qiang & Zhou, Wei-Xing & Sornette, Didier & Woodard, Ryan & Bastiaensen, Ken & Cauwels, Peter, 2010. "Bubble diagnosis and prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 74(3), pages 149-162, June.
    15. Grinblatt, Mark & Keloharju, Matti, 2000. "The investment behavior and performance of various investor types: a study of Finland's unique data set," Journal of Financial Economics, Elsevier, vol. 55(1), pages 43-67, January.
    16. Naylor, Michael J. & Rose, Lawrence C. & Moyle, Brendan J., 2007. "Topology of foreign exchange markets using hierarchical structure methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 199-208.
    17. Barrot, Jean-Noel & Kaniel, Ron & Sraer, David, 2016. "Are retail traders compensated for providing liquidity?," Journal of Financial Economics, Elsevier, vol. 120(1), pages 146-168.
    18. Onnela, J.-P. & Chakraborti, A. & Kaski, K. & Kertész, J., 2003. "Dynamic asset trees and Black Monday," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 247-252.
    19. Martin Dufwenberg & Tobias Lindqvist & Evan Moore, 2005. "Bubbles and Experience: An Experiment," American Economic Review, American Economic Association, vol. 95(5), pages 1731-1737, December.
    20. Heimo, Tapio & Kaski, Kimmo & Saramäki, Jari, 2009. "Maximal spanning trees, asset graphs and random matrix denoising in the analysis of dynamics of financial networks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(2), pages 145-156.
    21. Chiang, Thomas C. & Zheng, Dazhi, 2010. "An empirical analysis of herd behavior in global stock markets," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1911-1921, August.
    22. Carlota Perez, 2009. "The double bubble at the turn of the century: technological roots and structural implications," Cambridge Journal of Economics, Oxford University Press, vol. 33(4), pages 779-805, July.
    23. Nicol'o Musmeci & Vincenzo Nicosia & Tomaso Aste & Tiziana Di Matteo & Vito Latora, 2016. "The multiplex dependency structure of financial markets," Papers 1606.04872, arXiv.org.
    24. Dong-Ming Song & Michele Tumminello & Wei-Xing Zhou & Rosario N. Mantegna, 2011. "Evolution of worldwide stock markets, correlation structure and correlation based graphs," Papers 1103.5555, arXiv.org.
    25. Corgnet, Brice & Kujal, Praveen & Porter, David, 2010. "The effect of reliability, content and timing of public announcements on asset trading behavior," Journal of Economic Behavior & Organization, Elsevier, vol. 76(2), pages 254-266, November.
    26. Frank Emmert-Streib & Matthias Dehmer, 2010. "Influence of the Time Scale on the Construction of Financial Networks," PLOS ONE, Public Library of Science, vol. 5(9), pages 1-9, September.
    27. Anders Johansen & Didier Sornette, 2000. "The Nasdaq crash of April 2000: Yet another example of log-periodicity in a speculative bubble ending in a crash," Papers cond-mat/0004263, arXiv.org, revised May 2000.
    28. Gang-Jin Wang & Chi Xie & H. Eugene Stanley, 2018. "Correlation Structure and Evolution of World Stock Markets: Evidence from Pearson and Partial Correlation-Based Networks," Computational Economics, Springer;Society for Computational Economics, vol. 51(3), pages 607-635, March.
    29. Hoffmann, Arvid O.I. & Post, Thomas & Pennings, Joost M.E., 2013. "Individual investor perceptions and behavior during the financial crisis," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 60-74.
    30. Petter Holme, 2015. "Modern temporal network theory: a colloquium," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 88(9), pages 1-30, September.
    31. Jenna Birch & Athanasios A. Pantelous & Kimmo Soramäki, 2016. "Analysis of Correlation Based Networks Representing DAX 30 Stock Price Returns," Computational Economics, Springer;Society for Computational Economics, vol. 47(4), pages 501-525, April.
    32. Coelho, Ricardo & Gilmore, Claire G. & Lucey, Brian & Richmond, Peter & Hutzler, Stefan, 2007. "The evolution of interdependence in world equity markets—Evidence from minimum spanning trees," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 376(C), pages 455-466.
    33. Xi-Yuan Qian & Ya-Min Liu & Zhi-Qiang Jiang & Boris Podobnik & Wei-Xing Zhou & H. Eugene Stanley, 2015. "Detrended partial cross-correlation analysis of two nonstationary time series influenced by common external forces," Papers 1504.02435, arXiv.org, revised Apr 2015.
    34. Oechssler, Jörg & Schmidt, Carsten & Schnedler, Wendelin, 2011. "On the ingredients for bubble formation: Informed traders and communication," Journal of Economic Dynamics and Control, Elsevier, vol. 35(11), pages 1831-1851.
    35. N. Vandewalle & F. Brisbois & X. Tordoir, 2001. "Non-random topology of stock markets," Quantitative Finance, Taylor & Francis Journals, vol. 1(3), pages 372-374, March.
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