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Bear, Bull, Sidewalk, and Crash: The Evolution of the US Stock Market Using Over a Century of Daily Data

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  • Wang, Shixuan
  • Gupta, Rangan
  • Zhang, Yue-Jun

Abstract

In this paper, we employ a four-state hidden semi-Markov model, which outperforms a hidden Markov model, to identify market conditions of the US stock market over the daily period from 16th of February, 1885 to 4th of June, 2020. Our results indicate that the four hidden states represent bear-, bull-, sidewalk-, and crash-markets, which in turn appropriately capture the various major historical events during the period of study.

Suggested Citation

  • Wang, Shixuan & Gupta, Rangan & Zhang, Yue-Jun, 2021. "Bear, Bull, Sidewalk, and Crash: The Evolution of the US Stock Market Using Over a Century of Daily Data," Finance Research Letters, Elsevier, vol. 43(C).
    • Handle: RePEc:eee:finlet:v:43:y:2021:i:c:s1544612321000799
    DOI: 10.1016/j.frl.2021.101998
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    1. Yue‐Jun Zhang & Jin‐Liang Zhang, 2018. "Volatility forecasting of crude oil market: A new hybrid method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 781-789, December.
    2. Beatrice Simo-Kengne & Stephen Miller & Rangan Gupta & Mehmet Balcilar, 2016. "Evolution of the Monetary Transmission Mechanism in the US: the Role of Asset Returns," The Journal of Real Estate Finance and Economics, Springer, vol. 52(3), pages 226-243, April.
    3. Pierdzioch Christian & Gupta Rangan, 2020. "Uncertainty and Forecasts of U.S. Recessions," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 24(4), pages 1-20, September.
    4. Bulla, Jan & Bulla, Ingo, 2006. "Stylized facts of financial time series and hidden semi-Markov models," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2192-2209, December.
    5. Elena Andreou & Eric Ghysels & Andros Kourtellos, 2013. "Should Macroeconomic Forecasters Use Daily Financial Data and How?," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(2), pages 240-251, April.
    6. Vasilios Plakandaras & Juncal Cunado & Rangan Gupta & Mark E. Wohar, 2017. "Do leading indicators forecast U.S. recessions? A nonlinear re†evaluation using historical data," International Finance, Wiley Blackwell, vol. 20(3), pages 289-316, December.
    7. Liu, Zhenya & Wang, Shixuan, 2017. "Decoding Chinese stock market returns: Three-state hidden semi-Markov model," Pacific-Basin Finance Journal, Elsevier, vol. 44(C), pages 127-149.
    8. Bulla, Jan, 2006. "Application of Hidden Markov Models and Hidden Semi-Markov Models to Financial Time Series," MPRA Paper 7675, University Library of Munich, Germany.
    9. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    10. Gebka, Bartosz & Wohar, Mark E., 2019. "Stock return distribution and predictability: Evidence from over a century of daily data on the DJIA index," International Review of Economics & Finance, Elsevier, vol. 60(C), pages 1-25.
    11. Apergis, Nicholas & Gozgor, Giray & Lau, Chi Keung Marco & Wang, Shixuan, 2019. "Decoding the Australian electricity market: New evidence from three-regime hidden semi-Markov model," Energy Economics, Elsevier, vol. 78(C), pages 129-142.
    12. Zhenya Liu & Shixuan Wang, 2017. "Understanding the Chinese stock market: international comparison and policy implications," Economic and Political Studies, Taylor & Francis Journals, vol. 5(4), pages 441-455, October.
    13. Lau, Marco Chi Keung & Vigne, Samuel A. & Wang, Shixuan & Yarovaya, Larisa, 2017. "Return spillovers between white precious metal ETFs: The role of oil, gold, and global equity," International Review of Financial Analysis, Elsevier, vol. 52(C), pages 316-332.
    14. JÖrg Breitung & Christoph Roling, 2015. "Forecasting Inflation Rates Using Daily Data: A Nonparametric MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 34(7), pages 588-603, November.
    15. Babalos, Vassilios & Balcilar, Mehmet & Gupta, Rangan, 2015. "Herding behavior in real estate markets: Novel evidence from a Markov-switching model," Journal of Behavioral and Experimental Finance, Elsevier, vol. 8(C), pages 40-43.
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    Cited by:

    1. Kirby, Chris, 2023. "A closer look at the regime-switching evidence of bull and bear markets," Finance Research Letters, Elsevier, vol. 52(C).

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    More about this item

    Keywords

    Dow Jones Industrial Average; Hidden (semi-)Markov Models; Stock Returns; Market Conditions;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)

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