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Forecasting the market return direction based on a time-varying probability density model

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  • Gu, Wentao
  • Peng, Yiqing

Abstract

Forecasting the direction of stock returns is an important topic in the literature, and it is, to some extent, predictable. In this paper, we consider a new directional forecasting model that applies and extends the time-varying probability density function theory that was proposed by Harvey and Oryshchenko (2012). We capitalize on the relationship between the second order upper partial moment and the directional forecasts, and construct an adjustment mechanism for the forecasting model, which is an original work to the best of our knowledge. The empirical work using data from the Chinese stock market shows that both our forecasting benchmark model and the adjustment mechanism have statistically and economically significant out-of-sample predictive abilities for directional forecasts. Furthermore, the adjustment mechanism provides a great improvement and outperforms the benchmark model in general, and the results of the binary forecasting model are also provided for comparison purposes.

Suggested Citation

  • Gu, Wentao & Peng, Yiqing, 2019. "Forecasting the market return direction based on a time-varying probability density model," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    • Handle: RePEc:eee:tefoso:v:148:y:2019:i:c:s0040162519310741
    DOI: 10.1016/j.techfore.2019.119726
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    1. Demirer, Riza & Pierdzioch, Christian & Zhang, Huacheng, 2017. "On the short-term predictability of stock returns: A quantile boosting approach," Finance Research Letters, Elsevier, vol. 22(C), pages 35-41.
    2. Harvey, Andrew & Oryshchenko, Vitaliy, 2012. "Kernel density estimation for time series data," International Journal of Forecasting, Elsevier, vol. 28(1), pages 3-14.
    3. Nicholas Barberis & Ming Huang, 2008. "Stocks as Lotteries: The Implications of Probability Weighting for Security Prices," American Economic Review, American Economic Association, vol. 98(5), pages 2066-2100, December.
    4. Tina Hviid Rydberg & Neil Shephard, 2003. "Dynamics of Trade-by-Trade Price Movements: Decomposition and Models," The Journal of Financial Econometrics, Society for Financial Econometrics, vol. 1(1), pages 2-25.
    5. Peter F. Christoffersen & Francis X. Diebold, 2006. "Financial Asset Returns, Direction-of-Change Forecasting, and Volatility Dynamics," Management Science, INFORMS, vol. 52(8), pages 1273-1287, August.
    6. Newey, Whitney & West, Kenneth, 2014. "A simple, positive semi-definite, heteroscedasticity and autocorrelation consistent covariance matrix," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 33(1), pages 125-132.
    7. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    8. Jaehun Chung & Yongmiao Hong, 2007. "Model-free evaluation of directional predictability in foreign exchange markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 855-889.
    9. Holthausen, Duncan M, 1981. "A Risk-Return Model with Risk and Return Measured as Deviations from a Target Return," American Economic Review, American Economic Association, vol. 71(1), pages 182-188, March.
    10. Kang, Yuanfei, 2018. "Regulatory institutions, natural resource endowment and location choice of emerging-market FDI: A dynamic panel data analysis," Journal of Multinational Financial Management, Elsevier, vol. 45(C), pages 1-14.
    11. Fred D. Arditti, 1967. "Risk And The Required Return On Equity," Journal of Finance, American Finance Association, vol. 22(1), pages 19-36, March.
    12. Jing Zheng & Wentao Gu & Baolin Xu & Zongwu Cai, 2018. "The estimation for Lévy processes in high frequency data," Econometric Reviews, Taylor & Francis Journals, vol. 37(10), pages 1051-1066, November.
    13. Leung, Mark T. & Daouk, Hazem & Chen, An-Sing, 2000. "Forecasting stock indices: a comparison of classification and level estimation models," International Journal of Forecasting, Elsevier, vol. 16(2), pages 173-190.
    14. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578.
    15. Nyberg, Henri, 2011. "Forecasting the direction of the US stock market with dynamic binary probit models," International Journal of Forecasting, Elsevier, vol. 27(2), pages 561-578, April.
    16. Anatolyev, Stanislav & Gospodinov, Nikolay, 2010. "Modeling Financial Return Dynamics via Decomposition," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(2), pages 232-245.
    17. Merton, Robert C, 1981. "On Market Timing and Investment Performance. I. An Equilibrium Theory of Value for Market Forecasts," The Journal of Business, University of Chicago Press, vol. 54(3), pages 363-406, July.
    18. Leitch, Gordon & Tanner, J Ernest, 1991. "Economic Forecast Evaluation: Profits versus the Conventional Error Measures," American Economic Review, American Economic Association, vol. 81(3), pages 580-590, June.
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