Mid-Price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators

40 Pages Posted: 8 Aug 2018

See all articles by Adamantios Ntakaris

Adamantios Ntakaris

Tampere University of Technology

Juho Kanniainen

Tampere University

Moncef Gabbouj

Tampere University of Technology

Alexandros Iosifidis

Aarhus University

Date Written: June 13, 2018

Abstract

Stock price prediction is a challenging task, but machine learning methods have recently been used successfully for this purpose. In this paper, we extract over 270 hand-crafted features (factors) inspired by technical and quantitative analysis and tested their validity on short-term mid-price movement prediction. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. We also build a quantitative feature based on adaptive logistic regression for online learning, which is constantly selected first among the majority of the proposed feature selection methods. This study examines the best combination of features using high frequency limit order book data from Nasdaq Nordic. Our results suggest that sorting methods and classifiers can be used in such a way that one can reach the best performance with a combination of only very few informative features. This paper opens avenues for developing more advanced features combined with more sophisticated feature selection methods. It also provides helpful insight to market makers and traders in general by providing useful results that can be used to gain an information edge in trading.

Keywords: high-frequency trading, limit order book, mid-price, machine learning, technical analysis, quantitative analysis

JEL Classification: C00, C02, C12, C80, C90, D00

Suggested Citation

Ntakaris, Adamantios and Kanniainen, Juho and Gabbouj, Moncef and Iosifidis, Alexandros, Mid-Price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators (June 13, 2018). Available at SSRN: https://ssrn.com/abstract=3213389 or http://dx.doi.org/10.2139/ssrn.3213389

Adamantios Ntakaris (Contact Author)

Tampere University of Technology ( email )

Korkeakoulunkatu 8
P.O. 541
Tampere, 33101
Finland

Juho Kanniainen

Tampere University ( email )

P.O. 541, Korkeakoulunkatu 8 (Festia building)
Tampere, FI-33101
Finland

HOME PAGE: http://https://sites.google.com/site/juhokanniainen/

Moncef Gabbouj

Tampere University of Technology ( email )

P.O. 541, Korkeakoulunkatu 8 (Festia building)
Tampere, FI-33101
Finland

HOME PAGE: http://www.cs.tut.fi/~moncef

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