Trend Switching Processes in Financial Markets

  • Tobias Preis
  • H. Eugene Stanley


For an intriguing variety of switching processes in nature, the underlying complex system abruptly changes at a specific point from one state to another in a highly discontinuous fashion. Financial market fluctuations are characterized by many abrupt switchings creating increasing trends (“bubble formation”) and decreasing trends (“bubble collapse”), on time scales ranging from macroscopic bubbles persisting for hundreds of days to microscopic bubbles persisting only for very short time scales. Our analysis is based on a German DAX Future data base containing 13,991,275 transactions recorded with a time resolution of 10− 2 s. For a parallel analysis, we use a data base of all S&P500 stocks providing 2,592,531 daily closing prices. We ask whether these ubiquitous switching processes have quantifiable features independent of the time horizon studied. We find striking scale-free behavior of the volatility after each switching occurs. We interpret our findings as being consistent with time-dependent collective behavior of financial market participants. We test the possible universality of our result by performing a parallel analysis of fluctuations in transaction volume and time intervals between trades. We show that these financial market switching processes have features similar to those present in phase transitions. We find that the well-known catastrophic bubbles that occur on large time scales – such as the most recent financial crisis – are no outliers but in fact single dramatic representatives caused by the formation of upward and downward trends on time scales varying over nine orders of magnitude from the very large down to the very small.


Financial Market Market Participant Switching Point Future Contract Switching Process 
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.



The authors thank K. Binder, S.V. Buldyrev, C. De Grandi, S. Havlin, D. Helbing, U. Krey, H.-G. Matuttis, M.G. Mazza, I. Morgenstern, W. Paul, J.J. Schneider, R.H.R. Stanley, T. Vicsek, and G.M. Viswanathan for discussions, and we also thank the German Research Foundation (DFG), the Gutenberg Academy, and the NSF for financial support.


  1. 1.
    Anderson PW (1972) Science 177:393ADSCrossRefGoogle Scholar
  2. 2.
    Stanley HE (1971) Introduction to phase transitions and critical phenomena. Oxford University Press, LondonGoogle Scholar
  3. 3.
    Stanley HE (1999) Rev Mod Phys 71:358CrossRefGoogle Scholar
  4. 4.
    Stanley HE, Amaral LAN, Gabaix X, Gopikrishnan P, Plerou V, Rosenow B (1999) Physica A 301:126Google Scholar
  5. 5.
    Mantegna RN, Stanley HE (2000) Introduction to econophysics correlations and complexity in finance. Cambridge University Press, Cambridge, MAMATHGoogle Scholar
  6. 6.
    Axtell RL (2001) Science 293:1818ADSCrossRefGoogle Scholar
  7. 7.
    Takayasu H (ed) (2006) Practical fruits of econophysics. Springer, BerlinGoogle Scholar
  8. 8.
    Kiyono K, Struzik ZR, Yamamoto Y (2006) Phys Rev Lett 96:068701ADSCrossRefGoogle Scholar
  9. 9.
    Watanabe K, Takayasu H, Takayasu M (2007) Physica A 383:120MathSciNetADSCrossRefGoogle Scholar
  10. 10.
    Gabaix X, Gopikrishnan P, Plerou V, Stanley HE (2003) Nature 423:267ADSCrossRefGoogle Scholar
  11. 11.
    Preis T, Paul W, Schneider JJ (2008) Europhys Lett 82:68005ADSCrossRefGoogle Scholar
  12. 12.
    Preis T, Virnau P, Paul W, Schneider JJ (2009) New J Phys 11:093024CrossRefGoogle Scholar
  13. 13.
    Lillo F, Farmer JD, Mantegna RN (2003) Nature 421:129ADSCrossRefGoogle Scholar
  14. 14.
    Plerou V, Gopikrishnan P, Gabaix X, Stanley HE (2002) Phys Rev E 66:027104ADSCrossRefGoogle Scholar
  15. 15.
    Cont R, Bouchaud JP (2000) Macroecon Dyn 4:170CrossRefMATHGoogle Scholar
  16. 16.
    Krawiecki A, Holyst JA, Helbing D (2002) Phys Rev Lett 89:158701ADSCrossRefGoogle Scholar
  17. 17.
    O’Hara M (1995) Market microstructure theory. Blackwell, Cambridge, MAGoogle Scholar
  18. 18.
    Vandewalle N, Ausloos M (1997) Physica A 246:454ADSCrossRefGoogle Scholar
  19. 19.
    Eisler Z, Kertész J (2006) Phys Rev E 73:046109ADSCrossRefGoogle Scholar
  20. 20.
    Mandelbrot B (1963) J Business 36:394CrossRefGoogle Scholar
  21. 21.
    Fama EF (1963) J Business 36:420CrossRefGoogle Scholar
  22. 22.
    Lux T (1996) Appl Finan Econ 6:463ADSCrossRefGoogle Scholar
  23. 23.
    Guillaume DM, Dacorogna MM, Davé RR, Müller UA, Olsen RB, Pictet OV (1997) Fin Stochastics 1:95CrossRefMATHGoogle Scholar
  24. 24.
    Gopikrishnan P, Meyer M, Amaral L, Stanley HE (1998) Eur J Phys B 3:139ADSCrossRefGoogle Scholar
  25. 25.
    Plerou V, Gopikrishnan P, Rosenow B, Amaral LAN, Stanley HE (1999) Phys Rev Lett 83:1471ADSCrossRefGoogle Scholar
  26. 26.
    Gopikrishnan P, Plerou V, Amaral LAN, Meyer M, Stanley HE (1999) Phys Rev E 60:5305ADSCrossRefGoogle Scholar
  27. 27.
    Gopikrishnan P, Plerou V, Gabaix X, Stanley HE (2000) Phys Rev E 62:4493ADSCrossRefGoogle Scholar
  28. 28.
    Krugman P (1996) The self-organizing economy. Blackwell, Cambridge, MAGoogle Scholar
  29. 29.
    Shleifer A (2000) Inefficient markets: an introduction to behavioral finance. Oxford University Press, OxfordGoogle Scholar
  30. 30.
    Helbing D, Farkas I, Vicsek T (2000) Nature 407:487ADSCrossRefGoogle Scholar
  31. 31.
    Bunde A, Schellnhuber HJ, Kropp J (eds) (2002) The science of disasters: climate disruptions, heart attacks, and market crashes. Springer, BerlinGoogle Scholar
  32. 32.
    Jones CM, Kaul G, Lipson ML (1994) Rev Fin Stud 7:631CrossRefGoogle Scholar
  33. 33.
    Chan L, Fong WM (2000) J Fin Econ 57:247CrossRefGoogle Scholar
  34. 34.
    Politi M, Scalas E (2008) Physica A 387:2025ADSCrossRefGoogle Scholar
  35. 35.
    Jiang ZQ, Chen W, Zhou WX (2009) Physica A 388:433ADSCrossRefGoogle Scholar
  36. 36.
    Dubil R (2004) An arbitrage guide to financial markets. Wiley, ChichesterGoogle Scholar
  37. 37.
    Deutsch HP (2001) Derivate und interne modelle: modernes risk management. Schaefer-Poeschel, StuttgartCrossRefGoogle Scholar
  38. 38.
    Binder K (1987) Rep Prog Phys 50:783ADSCrossRefGoogle Scholar
  39. 39.
    Peng CK, Mietus J, Hausdorff JM, Havlin S, Stanley HE, Goldberger AL (1993) Phys Rev Lett 70:1343ADSCrossRefGoogle Scholar
  40. 40.
    Helbing D, Huberman BA (1998) Nature 396:738ADSCrossRefGoogle Scholar
  41. 41.
    Ivanov PC, Yuen A, Podobnik B, Lee Y (2004) Phys Rev E 69:056107ADSCrossRefGoogle Scholar
  42. 42.
    Smith E, Farmer JD, Gillemot L, Krishnamurthy S (2003) Quant Finance 3:481MathSciNetADSCrossRefGoogle Scholar
  43. 43.
    Lux T, Marchesi M (1999) Nature 397:498ADSCrossRefGoogle Scholar
  44. 44.
    Preis T, Golke S, Paul W, Schneider JJ (2006) Europhys Lett 75:510MathSciNetADSCrossRefGoogle Scholar
  45. 45.
    Preis T, Golke S, Paul W, Schneider JJ (2007) Phys Rev E 76:016108ADSCrossRefGoogle Scholar
  46. 46.
    Bouchaud JP, Matacz A, Potters M (2001) Phys Rev Lett 87:228701ADSCrossRefGoogle Scholar
  47. 47.
    Haerdle W, Kleinow T, Korostelev A, Logeay C, Platen E (2008) Quant Fin 8:81CrossRefMATHGoogle Scholar
  48. 48.
    Halla AD, Hautsch N (2007) J Fin Markets 10:249CrossRefGoogle Scholar
  49. 49.
    Preis T, Stanley HE (2009) J Stat Phys (Article in press) doi: 10.1007/s10955-009-9914-yGoogle Scholar
  50. 50.
    Preis T, Stanley HE (2009) APCTP Bulletin 23–24:18Google Scholar

Copyright information

© Springer 2010

Authors and Affiliations

  1. 1.Center for Polymer Studies, Department of PhysicsBoston UniversityBostonUSA
  2. 2.Institute of PhysicsJohannes Gutenberg University MainzMainzGermany
  3. 3.Artemis Capital Asset Management GmbHHolzheimGermany
  4. 4.Center for Polymer Studies and Department of PhysicsBoston UniversityBostonUSA

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