IDEAS home Printed from https://ideas.repec.org/a/bpj/sndecm/v1y1996i2n2.html
   My bibliography  Save this article

A Random Walk or Color Chaos on the Stock Market? Time-Frequency Analysis of S&P Indexes

Author

Listed:
  • Chen Ping

    (The University of Texas Austin, Texas)

Abstract

The random-walk (white-noise) model and the harmonic model are two polar models in linear systems. A model in between is color chaos, which generates irregular oscillations with a narrow frequency (color) band. Time-frequency analysis is introduced for evolutionary time-series analysis. The deterministic component from noisy data can be recovered by a time-variant filter in Gabor space. The characteristic frequency is calculated from the Wigner decomposed distribution series. It is found that about 70 percent of fluctuations in Standard & Poor stock price indexes, such as the FSPCOM and FSDXP monthly series, detrended by the Hodrick-Prescott (HP) filter, can be explained by deterministic color chaos. The characteristic period of persistent cycles is around three to four years. Their correlation dimension is about 2.5. The existence of persistent chaotic cycles reveals a new perspective of market resilience and new sources of economic uncertainties. The nonlinear pattern in the stock market may not be wiped out by market competition under nonequilibrium situations with trend evolution and frequency shifts. The color-chaos model of stock-market movements may establish a potential link between business-cycle theory and asset-pricing theory.

Suggested Citation

  • Chen Ping, 1996. "A Random Walk or Color Chaos on the Stock Market? Time-Frequency Analysis of S&P Indexes," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 1(2), pages 1-19, July.
    • Handle: RePEc:bpj:sndecm:v:1:y:1996:i:2:n:2
    DOI: 10.2202/1558-3708.1014
    as

    Download full text from publisher

    File URL: https://doi.org/10.2202/1558-3708.1014
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.
    File URL: https://libkey.io/10.2202/1558-3708.1014?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Jason Angelopoulos, 2017. "Time–frequency analysis of the Baltic Dry Index," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(2), pages 211-233, June.
    2. Harrison, Robert G. & Yu, Dejin & Oxley, Les & Lu, Weiping & George, Donald, 1999. "Non-linear noise reduction and detecting chaos: some evidence from the S&P Composite Price Index," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 48(4), pages 497-502.
    3. Ping Chen, 2014. "Metabolic growth theory: market-share competition, learning uncertainty, and technology wavelets," Journal of Evolutionary Economics, Springer, vol. 24(2), pages 239-262, April.
    4. Greg Hannsgen & Tai Young-Taft, 2015. "Inside Money in a Kaldor-Kalecki-Steindl Fiscal Policy Model: The Unit of Account, Inflation, Leverage, and Financial Fragility," Economics Working Paper Archive wp_839, Levy Economics Institute.
    5. Tangian, Andranik, 2008. "Predicting DAX trends from Dow Jones data by methods of the mathematical theory of democracy," European Journal of Operational Research, Elsevier, vol. 185(3), pages 1632-1662, March.
    6. Antoniou, Antonios & Vorlow, Constantinos E., 2004. "Recurrence quantification analysis of wavelet pre-filtered index returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 257-262.
    7. Ignacio Escanuela Romana & Clara Escanuela Nieves, 2023. "A spectral approach to stock market performance," Papers 2305.05762, arXiv.org.
    8. Yinghao LUO, 2016. "Nonlinear Trend and Purchasing Power Parity," Journal of Economics Bibliography, KSP Journals, vol. 3(3), pages 490-497, September.
    9. Ali Moeini & Mehdi Ahrari & Saeed Sadat Madarshahi3, 2007. "Investigating Chaos in Tehran Stock Exchange Index," Iranian Economic Review (IER), Faculty of Economics,University of Tehran.Tehran,Iran, vol. 12(1), pages 103-120, winter.
    10. Kapil Gupta & Balwinder Singh, 2009. "Information Memory and Pricing Efficiency of Futures Contracts," Journal of Emerging Market Finance, Institute for Financial Management and Research, vol. 8(2), pages 191-250, May.
    11. Antonios Antoniou & Constantinos E. Vorlow, 2004. "Price Clustering and Discreteness: Is there Chaos behind the Noise?," Papers cond-mat/0407471, arXiv.org.
    12. Tahsina Haque Simu, 2012. "Randomness in CASPI (CSE All Share Price Index): An Empirical Study," Indian Journal of Commerce and Management Studies, Educational Research Multimedia & Publications,India, vol. 3(2), pages 01-05, May.
    13. Tao You & Paweł Fiedor & Artur Hołda, 2015. "Network Analysis of the Shanghai Stock Exchange Based on Partial Mutual Information," JRFM, MDPI, vol. 8(2), pages 1-19, June.
    14. Carlos Pedro Gonc{c}alves, 2018. "Financial Risk and Returns Prediction with Modular Networked Learning," Papers 1806.05876, arXiv.org.
    15. Scarlat, E.I. & Stan, Cristina & Cristescu, C.P., 2007. "Chaotic features in Romanian transition economy as reflected onto the currency exchange rate," Chaos, Solitons & Fractals, Elsevier, vol. 33(2), pages 396-404.
    16. Antoniou, Antonios & Vorlow, Constantinos E., 2005. "Price clustering and discreteness: is there chaos behind the noise?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 389-403.
    17. Gurmeet Singh, 2017. "Estimating Optimal Hedge Ratio and Hedging Effectiveness in the NSE Index Futures," Jindal Journal of Business Research, , vol. 6(2), pages 108-131, December.
    18. Sharif Md. Raihan & Yi Wen & Bing Zeng, 2005. "Wavelet: a new tool for business cycle analysis," Working Papers 2005-050, Federal Reserve Bank of St. Louis.
    19. Tan, Zhengxun & Liu, Juan & Chen, Juanjuan, 2021. "Detecting stock market turning points using wavelet leaders method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sndecm:v:1:y:1996:i:2:n:2. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Peter Golla (email available below). General contact details of provider: https://www.degruyter.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.