Machine learning for forecasting mid-price movements using limit order book data

Paraskevi Nousi*, Avraam Tsantekidis*, Nikolaos Passalis, Adamantios Ntakaris, Juho Kanniainen, Anastasios Tefas, Moncef Gabbouj, Alexandros Iosifidis

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
Article number8713851
Pages (from-to)64722-64736
Number of pages15
JournalIEEE Access
Publication statusPublished - 14 May 2019


  • feature extraction
  • machine learning
  • data models
  • task analysis
  • machine learning algorithms
  • predictive models
  • forecasting


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