Kernel Modification Effects for Support Vector Machine Applied to Limit Order Book of Nikkei 225 Futures
Hayato Kijima1, Hideyuki Takada2
1. Graduate School of Information Science, Toho University, Miyama 2-2-1, Chiba 274-8510, Funabashi, Japan.
2. Department of Information Science, Toho University,Miyama 2-2-1, Chiba 274-8510, Funabashi, Japan.
Abstract: Market participants place their limit/market orders by taking into account both the trajectory and current status of the limit order book. This behavior is based on the policy that the shape of the limit order book is quite informative for predicting future direction of a traded asset. In this paper, we employ Support Vector Machine to learn future mid-price directions and apply conformal transformation of the kernel function in order to improve its accuracy. Our empirical studies are based on Nikkei 225 futures and show that the conformal transform methods improved the precision more than 3% in average compared to the standard Gaussian RBF kernel. We further investigate numerically how the precision is improved by controlling parameter involved in the conformal transform.
Keywords: Limit Order Book, Support Vector Machine, Riemannian metric,Conformal transformation.
Pages: 149 – 167 | Full PDF Paper