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

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Details

Original languageEnglish
Pages (from-to)64722-64736
JournalIEEE Access
Volume7
DOIs
Publication statusPublished - 2019
Publication typeA1 Journal article-refereed

Abstract

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.

Publication forum classification

Field of science, Statistics Finland