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Mid-price prediction based on machine learning methods with technical and quantitative indicators

Author

Listed:
  • Adamantios Ntakaris
  • Juho Kanniainen
  • Moncef Gabbouj
  • Alexandros Iosifidis

Abstract

Stock price prediction is a challenging task, in which machine learning methods have recently been successfully used. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical indicators and quantitative analysis and test their validity on short-term mid-price movement prediction for Nordic TotalView-ITCH stocks. The suggested feature list represents one of the most extensive studies in the field of financial feature engineering. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also introduce a novel quantitative feature based on adaptive logistic regression for online learning. The proposed feature is consistently selected as the first feature among a large number of indicators used in this study. We further examine the best combinations of features using a high-frequency limit order book Nordic database. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best classification performance with a combination of only a few advanced hand-crafted features.

Suggested Citation

  • Adamantios Ntakaris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2020. "Mid-price prediction based on machine learning methods with technical and quantitative indicators," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-39, June.
    • Handle: RePEc:plo:pone00:0234107
    DOI: 10.1371/journal.pone.0234107
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    References listed on IDEAS

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    2. Martin D. Gould & Mason A. Porter & Stacy Williams & Mark McDonald & Daniel J. Fenn & Sam D. Howison, 2013. "Limit order books," Quantitative Finance, Taylor & Francis Journals, vol. 13(11), pages 1709-1742, November.
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