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

Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data