A Dynamic, Volume-Weighted Average Price Approach Based on the Fast Fourier Transform Algorithm

摘要

We propose a model for decomposing a volume series based on the Fast Fourier Transform (FFT) algorithm. By setting a threshold for the power spectrum, the model extracts the periodic and nonperiodic components from the original volume series and then predicts them. By analyzing samples from four major stock indices, we find that a too small threshold and a too large threshold cause negative effects on the performance of the FFT model. Appropriate thresholds are found at approximately the 93rd to 95th percentile for the four indices studied. The out-of-sample experiment for the 50 stocks of the Shanghai 50 Composite Index shows that the FFT model is superior to the classic moving average model in terms of both volume prediction and Volume-weighted Average Price (VWAP) tracking accuracy. Meanwhile, for almost all of the 50 stocks, the FFT model outperforms the Bialkowski etal. (2008) model in terms of volume-prediction accuracy. The two models perform comparably in terms of the VWAP tracking error.