Machine Learning for Forecasting Mid-Price Movements Using Limit Order Book Data

Abstract

Forecasting the movements of stock prices is one of 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 the 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 the ML highly befits this task carving the path towards future research in this field.

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