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

PloS One
Adamantios NtakarisAlexandros 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.

References

Nov 1, 1994·Australian and New Zealand Journal of Ophthalmology·R F Lowe
Jun 14, 2000·American Journal of Physiology. Heart and Circulatory Physiology·J S Richman, J R Moorman
Oct 21, 2004·Nurse Researcher·Rosemarie StreetonJackie Campbell
Jan 1, 1994·IEEE Transactions on Neural Networks·R Battiti
Feb 16, 2008·Doklady. Biochemistry and Biophysics·A A Rubashkin, P Iserovich

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Methods Mentioned

BETA
feature extraction

Software Mentioned

HFT
ZLEMA
EMA
LDA2
ITCH
LDA1

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