  Forecasting the movements of stock prices is one the most challenging
problems in financial markets analysis. In this paper, we use Machine Learning
(ML) algorithms for the prediction of future price movements using limit order
book data. Two different sets of features are combined and evaluated:
handcrafted features based on the raw order book data and features extracted by
ML algorithms, resulting in feature vectors with highly variant
dimensionalities. Three classifiers are evaluated using combinations of these
sets of features on two different evaluation setups and three prediction
scenarios. Even though the large scale and high frequency nature of the limit
order book poses several challenges, the scope of the conducted experiments and
the significance of the experimental results indicate that Machine Learning
highly befits this task carving the path towards future research in this field.
