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Forecasting Bull and Bear Markets: Evidence from China

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  • Xiaojian Yu
  • Zewei Chen
  • Weidong Xu
  • Junhui Fu

Abstract

This article extends previous empirical research to forecast Chinese bull and bear stock markets by using three types of binary probit time series models, which are static, autoregressive, and dynamic autoregressive models. This study shows that the dynamic auto regressive model performs the best both in- and out-of-sample. The inflation and market return variables significantly affect the market forecast. The dynamic autoregressive model has successfully forecast the bull and bear markets since 2007. The investment strategy based on this model performs better than the simple buy-and-hold strategy, especially after the Chinese government reformed the non-tradable shares in 2005.

Suggested Citation

  • Xiaojian Yu & Zewei Chen & Weidong Xu & Junhui Fu, 2017. "Forecasting Bull and Bear Markets: Evidence from China," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 53(8), pages 1720-1733, August.
    • Handle: RePEc:mes:emfitr:v:53:y:2017:i:8:p:1720-1733
    DOI: 10.1080/1540496X.2016.1184141
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    Cited by:

    1. Mishelle Doorasamy & Prince Kwasi Sarpong, 2018. "Fractal Market Hypothesis and Markov Regime Switching Model: A Possible Synthesis and Integration," International Journal of Economics and Financial Issues, Econjournals, vol. 8(1), pages 93-100.

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