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Journal of Applied Nonlinear Dynamics
Miguel A. F. Sanjuan (editor), Albert C.J. Luo (editor)
Miguel A. F. Sanjuan (editor)

Department of Physics, Universidad Rey Juan Carlos, 28933 Mostoles, Madrid, Spain

Email: miguel.sanjuan@urjc.es

Albert C.J. Luo (editor)

Department of Mechanical and Industrial Engineering, Southern Illinois University Ed-wardsville, IL 62026-1805, USA

Fax: +1 618 650 2555 Email: aluo@siue.edu


Detecting Chaos and Nonlinear Dynamics in Sao Paulo Stock Exchange Index Returns (IBOVESPA)

Journal of Applied Nonlinear Dynamics 7(1) (2018) 45--58 | DOI:10.5890/JAND.2018.03.004

Ferrouhi El Mehdi

Faculty of Law, Economics and Social Sciences, Ibn Tofail University, Kenitra, Morocco

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Abstract

The market efficiency theory states that stock returns on efficient markets are random, and then, we cannot predict accurately their future evolutions. Chaos theory is considered as a remedy for this insufficiency since the evolution of chaotic systems appears random but follows precise rules. After the application of detection’s tools of deterministic chaos to IBOVESPA returns, we obtained a fractal dimension equal to 2.5, the convergence of Lyapunov exponent towards positive values and first Lyapunov exponent characterized by its positivity. Results obtained after the application of the Nearest Neighbors method allows us to conclude IBOVESPA returns are characterized by sensitivity to initial conditions and are therefore chaotic.

References

  1. [1]  Fama, E.F. (1965), Random walks in stock-market prices, N°16 Selected papers, University of Chicago Graduate School of Business, 55-59.
  2. [2]  Peters, E. (1991), A Chaotic attractor for the S&P 500, Finance Analysis Journal, 47, 55-81.
  3. [3]  Taramasco, O. and Girerd-Potin, I. (1994), Les rentabilités `à la bourse de Paris sont-elles chaotiques?, Revue économique, 45(2), 215-238. (in French).
  4. [4]  Scheinkman, J.A. and Lebaron, B. (1989), Nonlinear dynamics and stock returns, The journal of business, 62, 311-337.
  5. [5]  Harrison, R. et al. (1999), Non-linear noise reduction and detecting chaos: some evidence from the S&P Composite Price Index, Mathematics and Computers in Simulation, 48, 497-502.
  6. [6]  Hsieh, D. (1991), Chaos and Nonlinear Dynamics: Application to Financial Markets, The Journal of Finance, 46, 1839-1877.
  7. [7]  Kumar, N. (1996), Deterministic Chaos, Universities Press, India.
  8. [8]  Bouquiaux, L. (1994), L'harmonie et le chaos: le rationalisme leibnizien et la nouvelle science, Peeters: Paris. (in French).
  9. [9]  Hilborn, R.C. (2000), Chaos and nonlinear dynamics: an introduction for scientists and engineers, Oxford University Press: Oxford
  10. [10]  Poincaré, H. (1908), Science and method, Thomas Nelson and Sons: London.
  11. [11]  Takens, F. (1981), Detecting strange attractors in turbulence, Dynamical Systems and Turbulence, eds. Rand, D.A. & Young, L.S. (Springer-Verlag, Warwick), 366-381.
  12. [12]  Packard, N.H., Crutchfield, J.P., Farmer, J.D., and Shaw, R.S. (1980), Geometry from time series, Physics Review Letters, 45, 712-716.
  13. [13]  Grassberger, P. and Procaccia, I. (1983), Characterization of strange attractors, Physics Review Letters, 50, 346-349
  14. [14]  Wolf, A., Swift, J.B., Swinney, H.L., and Vastano, J.A. (1985), Determining Lyapunov exponents from a time series, Physica. 16, 285-317.
  15. [15]  Brock, W.A., Dechert, W.D., Scheinkman, J.A., and LeBaron, B. (1996), A test for independence based on the correlation dimension, Econometric Reviews, 15, 197-235
  16. [16]  Kanzler, L. (1999), Very fast and correctly sized estimation of the BDS statistic, Tech. Rep., Christ Church and Department of Economics, University of Oxford.
  17. [17]  Farmer, J.D and Sidorowich, J.J. (1987), Predicting chaotic time series, Physics Review Letters, 59(8), 845- 848
  18. [18]  Girerd-Potin, I. and Taramasco, O. (1994), Les rentabilités à la bourse de Paris sont-elles chaotiques?, Revue Economique, 45, 215-238. (in French).