  With the proliferation of algorithmic high-frequency trading in financial
markets, the Limit Order Book has generated increased research interest.
Research is still at an early stage and there is much we do not understand
about the dynamics of Limit Order Books. In this paper, we employ a machine
learning approach to investigate Limit Order Book features and their potential
to predict short term price movements. This is an initial broad-based
investigation that results in some novel observations about LOB dynamics and
identifies several promising directions for further research. Furthermore, we
obtain prediction results that are significantly superior to a baseline
predictor.
