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Market turning points forecasting using wavelet analysis

Author

Listed:
  • Bai, Limiao
  • Yan, Sen
  • Zheng, Xiaolian
  • Chen, Ben M.

Abstract

Based on the system adaptation framework we previously proposed, a frequency domain based model is developed in this paper to forecast the major turning points of stock markets. This system adaptation framework has its internal model and adaptive filter to capture the slow and fast dynamics of the market, respectively. The residue of the internal model is found to contain rich information about the market cycles. In order to extract and restore its informative frequency components, we use wavelet multi-resolution analysis with time-varying parameters to decompose this internal residue. An empirical index is then proposed based on the recovered signals to forecast the market turning points. This index is successfully applied to US, UK and China markets, where all major turning points are well forecasted.

Suggested Citation

  • Bai, Limiao & Yan, Sen & Zheng, Xiaolian & Chen, Ben M., 2015. "Market turning points forecasting using wavelet analysis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 437(C), pages 184-197.
    • Handle: RePEc:eee:phsmap:v:437:y:2015:i:c:p:184-197
    DOI: 10.1016/j.physa.2015.05.027
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    Cited by:

    1. 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).
    2. Concepción González-Concepción & María Candelaria Gil-Fariña & Celina Pestano-Gabino, 2018. "Wavelet power spectrum and cross-coherency of Spanish economic variables," Empirical Economics, Springer, vol. 55(2), pages 855-882, September.
    3. Bartoš, Erik & Pinčák, Richard, 2017. "Identification of market trends with string and D2-brane maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 57-70.

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