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Forecasting trading volume in the Chinese stock market based on the dynamic VWAP

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  • Ye Xunyu

    (School of Systems Science, Beijing Normal University, Beijing, China)

  • Yan Rui
  • Li Handong

    (School of Government, Beijing Normal University, Beijing, China)

Abstract

We investigate the modeling and forecasting of the intra-daily volume time series in Chinese stock market with an application to dynamic Volume Weighted Average Price (VWAP) method. The empirical results show that: (1) This method performs better than the traditional static VWAP strategy; (2) By adjusting time scale (time window) and the composition of the stock portfolio according to the principal component analysis method, we can further improve the forecasting accuracy of the stock turnover series; (3) There is significant long memory characteristic in the special component of the turnover series when using the dynamic VWAP method, however, we find that it can not improve the prediction of turnover series by using ARFIMA model on these series. We also analyze the reasons and provide some explanations.

Suggested Citation

  • Ye Xunyu & Yan Rui & Li Handong, 2014. "Forecasting trading volume in the Chinese stock market based on the dynamic VWAP," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 18(2), pages 1-20, April.
    • Handle: RePEc:bpj:sndecm:v:18:y:2014:i:2:p:20:n:5
    DOI: 10.1515/snde-2013-0023
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    References listed on IDEAS

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    1. Lo, Andrew W & Wang, Jiang, 2000. "Trading Volume: Definitions, Data Analysis, and Implications of Portfolio Theory," The Review of Financial Studies, Society for Financial Studies, vol. 13(2), pages 257-300.
    2. Bialkowski, Jedrzej & Darolles, Serge & Le Fol, Gaëlle, 2008. "Improving VWAP strategies: A dynamic volume approach," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1709-1722, September.
    3. Biais, Bruno & Hillion, Pierre & Spatt, Chester, 1995. "An Empirical Analysis of the Limit Order Book and the Order Flow in the Paris Bourse," Journal of Finance, American Finance Association, vol. 50(5), pages 1655-1689, December.
    4. Granger, C. W. J., 1980. "Long memory relationships and the aggregation of dynamic models," Journal of Econometrics, Elsevier, vol. 14(2), pages 227-238, October.
    5. Konishi, Hizuru, 2002. "Optimal slice of a VWAP trade," Journal of Financial Markets, Elsevier, vol. 5(2), pages 197-221, April.
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    Cited by:

    1. Liang Zhao & Wei Li & Ruihan Bao & Keiko Harimoto & YunfangWu & Xu Sun, 2021. "Long-term, Short-term and Sudden Event: Trading Volume Movement Prediction with Graph-based Multi-view Modeling," Papers 2108.11318, arXiv.org.

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