Improvement of Support Vector Machine Applied to Limit Order Book

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 combined with conformally transformed Gaussian RBF kernel to forecast the mid price dynamics. Our empirical studies show that the conformal transform methods improved the precision more than 3% in average compared to the standard Gaussian RBF kernel.