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Crash forecasting in the Korean stock market based on the log-periodic structure and pattern recognition

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  • Ko, Bonggyun
  • Song, Jae Wook
  • Chang, Woojin

Abstract

The aim of this research is to propose an alarm index to forecast the crash of the Korean financial market in extension to the idea of Johansen–Ledoit–Sornette model, which uses the log-periodic functions and pattern recognition algorithm. We discover that the crashes of the Korean financial market can be classified into domestic and global crises where each category requires different window length of fitted datasets. Therefore, we add the window length as a new parameter to enhance the performance of alarm index. Distinguishing the domestic and global crises separately, our alarm index demonstrates more robust forecasting than previous model by showing the error diagram and the results of trading performance.

Suggested Citation

  • Ko, Bonggyun & Song, Jae Wook & Chang, Woojin, 2018. "Crash forecasting in the Korean stock market based on the log-periodic structure and pattern recognition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 308-323.
    • Handle: RePEc:eee:phsmap:v:492:y:2018:i:c:p:308-323
    DOI: 10.1016/j.physa.2017.09.074
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    Citations

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    Cited by:

    1. Molina-Muñoz, Jesús & Mora-Valencia, Andrés & Perote, Javier, 2020. "Market-crash forecasting based on the dynamics of the alpha-stable distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    2. Pawel Dlotko & Simon Rudkin, 2019. "The Topology of Time Series: Improving Recession Forecasting from Yield Spreads," Working Papers 2019-02, Swansea University, School of Management.
    3. Ko, Bonggyun & Song, Jae Wook, 2018. "A simple analytics framework for evaluating mean escape time in different term structures with stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 398-412.

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