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A new indicator of imminent occurrence of drawdown in the stock market

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  • Caetano, Marco Antonio Leonel
  • Yoneyama, Takashi

Abstract

The crashes in financial markets have caught the attention of many researchers since 1929 and several mathematical models have been proposed to try to forecast the occurrence of these events. The main idea in this work is to use a wavelet transform to detect imminent abrupt changes in a financial time series, which may be eventually related to the possibility of a crash. Case studies are conducted using wavelet approaches with data covering pre-crash and post-crash 1929, as well as more recent Hang Seng and IBOVESPA data. The financial crisis of 2008 also is analyzed using this method. These time series provide useful insights into the behavior of wavelet coefficients under the possibility of short term crashes in stock market. However, it is not a trivial task to infer an imminent drawdown by simply examining the pattern of the wavelet transform coefficients. Hence, an index (a real number between 0 and 1) is proposed to aggregate the information provided by the wavelet coefficients. The new index presented good capability of monitoring crashes and drawdown with small error margins, at least in the studied cases.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:phsmap:v:388:y:2009:i:17:p:3563-3571
    DOI: 10.1016/j.physa.2009.05.024
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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, 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.
    3. Jia Zhai & Yi Cao & Xuemei Ding, 2018. "Data analytic approach for manipulation detection in stock market," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 897-932, April.

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