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Characterizing abrupt changes in the stock prices using a wavelet decomposition method

Author

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  • Caetano, Marco Antonio Leonel
  • Yoneyama, Takashi

Abstract

Abrupt changes in the stock prices, either upwards or downwards, are usually preceded by an oscillatory behavior with frequencies that tend to increase as the moment of transition becomes closer. The wavelet decomposition methods may be useful for analysis of this oscillations with varying frequencies, because they provide simultaneous information on the frequency (scale) and localization in time (translation). However, in order to use the wavelet decomposition, certain requirements have to be satisfied, so that the linear and cyclic trends are eliminated by standard least squares techniques. The coefficients obtained by the wavelet decomposition can be represented in a graphical form. A threshold can then be established to characterize the likelihood of a short-time abrupt change in the stock prices. Actual data from the São Paulo Stock Exchange (Bolsa de Valores de São Paulo) were used in this work to illustrate the proposed method.

Suggested Citation

  • Caetano, Marco Antonio Leonel & Yoneyama, Takashi, 2007. "Characterizing abrupt changes in the stock prices using a wavelet decomposition method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 383(2), pages 519-526.
    • Handle: RePEc:eee:phsmap:v:383:y:2007:i:2:p:519-526
    DOI: 10.1016/j.physa.2007.03.027
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    Citations

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    Cited by:

    1. Caetano, Marco Antonio Leonel & Yoneyama, Takashi, 2012. "A method for detection of abrupt changes in the financial market combining wavelet decomposition and correlation graphs," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4877-4882.
    2. Caetano, Marco Antonio Leonel & Yoneyama, Takashi, 2009. "A new indicator of imminent occurrence of drawdown in the stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(17), pages 3563-3571.
    3. Chen, Xinxin & Guo, Yanhong & Song, Yingying, 2024. "Multiple time scales investor sentiment impact the stock market index fluctuation: From margin trading business perspective," The North American Journal of Economics and Finance, Elsevier, vol. 69(PA).
    4. Caetano, Marco Antonio Leonel & Yoneyama, Takashi, 2015. "Boolean network representation of contagion dynamics during a financial crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 417(C), pages 1-6.
    5. Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022. "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, vol. 17(1), pages 1-23, Enero - M.

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