Advertisement

Detecting Environmental Changes through High-Resolution Data of Financial Markets

  • Aki-Hiro Sato
Part of the Studies in Computational Intelligence book series (SCI, volume 199)

Abstract

This article proposes methods to detect states of financial markets both comprehensively and with a high-resolution. In order to quantify trading patterns several mathematical methods are proposed based on frequencies of quotations/ transactions estimated from high-resolution data of financial markets. The empirical results (graphical network representation and quantification of states of market participants) for the foreign exchange market are shown. It is concluded that synchronous behavior associated with a large population of market participants may be a candidate of precursory signs leading to an environmental change.

Keywords

Bipartite Graph Market Participant Physical Review Letter Foreign Exchange Market Synchronous State 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Carbone, A., Kaniadakis, G., Scarfone, A.M.: Tails and Ties, Editorial — Topical Issue on Physics in Society. The European Physical Journal B 57, 121–125 (2007)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Lambiotte, R., Ausloos, M., Thelwall, M.: Word statistics in Blogs and RSS feeds: Towards empirical universal evidence. Journal of Informatics 1, 277–286 (2007)Google Scholar
  3. 3.
    Mantegna, R.N., Stanley, H.E.: An Introduction to Econophyscis: Correlations and complexity in finance. Cambridge University Press, Cambridge (1999)Google Scholar
  4. 4.
    Souma, W., Fujiwara, Y., Aoyama, H.: Random matrix approach to shareholding networks. Physica A 344, 73–76 (2004)CrossRefMathSciNetGoogle Scholar
  5. 5.
    Mizuno, T., Takayasu, H., Takayasu, M.: Correlation networks among currencies. Physica A 364, 336–342 (2006)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Hayashi, K., Kaizoji, T., Pichl, L.: Correlation patterns of NIKKEI index constituents: Towards a mean-field model. Physica A 383, 16–21 (2007)CrossRefGoogle Scholar
  7. 7.
    Naylor, M.J., Rose, L.C., Moyle, B.J.: Topology of foreign exchange markets using hierarchical structure methods. Physica A 382, 199–208 (2007)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Sato, A.-H., Hołyst, J.A.: Characteristic periodicities of collective behavior at the foreign exchange market. The European Physical Journal B 62, 373–380 (2008)CrossRefGoogle Scholar
  9. 9.
    Heimo, T., Tibély, G., Saramäki, J., Kaski, K., Kertész, J.: Spectral methods and cluster structure in correlation-based networks. Physica A 387, 5930–5945 (2008)CrossRefGoogle Scholar
  10. 10.
    Sato, A.-H.: Comprehensive high-resolution analysis on behavior of market participants in the foreign exchange market (in preparation)Google Scholar
  11. 11.
  12. 12.
    Albert, R., Barabási, A.-L.: Statistical mechanics of complex networks. Reviews of Modern Physics 74, 47–97 (2002)CrossRefMathSciNetGoogle Scholar
  13. 13.
    Noh, J.D., Rieger, H.: Random walks on complex networks. Physical Review Letters 92, 118701 (2004)CrossRefGoogle Scholar
  14. 14.
    Menezes, M.A., Barabáshi, A.-L.: Separating internal and external dynamics of complex systems. Physical Review Letters 93, 068701 (2004)CrossRefGoogle Scholar
  15. 15.
    Eisler, Z., Kertész, J.: Scaling theory of temporal correlations and size-dependent fluctuations in the traded value of stocks. Physical Review E 73, 046109 (2006)CrossRefGoogle Scholar
  16. 16.
    Meloni, S., Gómez-Gradeñes, J., Latora, V., Moreno, Y.: Scaling breakdown in flow fluctuations on complex networks. Physical Review Letters 100, 208701 (2008)CrossRefGoogle Scholar
  17. 17.
    Sornette, D., Deschâtres, F., Gilbert, T., Ageon, Y.: Endogenous versus exogenous shocks in complex networks — an empirical test using book sale rankings. Physical Review Letters 93, 228701 (2004)CrossRefGoogle Scholar
  18. 18.
    Lux, T.: Herd behavior, bullbes and crashes. Economic Journal 105, 881–896 (1995)CrossRefGoogle Scholar
  19. 19.
    Schumpeter, J.A.: Theory of Economic Development: An inquiry into profits, capital, credit, interest and the business cycle. Oxford University Press, Oxford (1961) (Originally published in 1912)Google Scholar
  20. 20.
    Honggang, L., Gao, Y.: A GDP fluctuation model based on interacting firms. Physica A 387, 5225–5230 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Aki-Hiro Sato
    • 1
  1. 1.Department of Applied Mathematics and Physics, Graduate School of InformaticsKyoto University 

Personalised recommendations