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

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.