Intraday Volume Percentages Forecasting Using a Dynamic SVM-Based Approach

作     者：LIU Xiaotao LAI Kin Keung 

作者机构：Department of Finance Central China Normal University International Business School Shaanxi Normal University Department of Management Sciences City University of Hong Kong 

出 版 物：《Journal of Systems Science & Complexity》 (系统科学与复杂性学报（英文版）)

年 卷 期：2017年第30卷第2期

页      面：421-433页

核心收录：

学科分类：02[经济学] 0202[经济学-应用经济学] 020202[经济学-区域经济学] 

主　　题：Intraday volume percentages principal component decomposition SVM VWAP. 

摘      要：This paper proposes a dynamic model to forecast intraday volume percentages by decomposing the trade volume into two parts: The average part as the intraday volume pattern and the residual term as the abnormal changes. An empirical test on data spanning half-a-year gold futures and S&P 500 futures reveals that a rolling average of the previous days volume percentages shows great predictive ability for the average part. An SVM approach with the input pattern consisting of two categories is employed to forecast the residual term. One is the previous days volume percentages in the same time interval and the other is the most recent volume percentages. The study shows that this dynamic SVM-based forecasting approach outperforms the other commonly used statistical methods and enhances the tracking performance of a VWAP strategy greatly.