![]() ![]() The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Ĭompeting interests: The authors have declared that no competing interests exist. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.įunding: AN, JK, MG, AI MSCA-ITN-ETN 675044 The research leading to these results has received funding from the H2020 Project BigDataFinance MSCA-ITN-ETN 675044 ( ), Training for Big Data in Financial Research and Risk Management. Received: AugAccepted: Published: June 12, 2020Ĭopyright: © 2020 Ntakaris et al. PLoS ONE 15(6):Įditor: Alejandro Raul Hernandez Montoya, 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.Ĭitation: Ntakaris A, Kanniainen J, Gabbouj M, Iosifidis A (2020) Mid-price prediction based on machine learning methods with technical and quantitative indicators. We further examine the best combinations of features using a high-frequency limit order book Nordic database. The proposed feature is consistently selected as the first feature among a large number of indicators used in this study. We also introduce a novel quantitative feature based on adaptive logistic regression for online learning. We focus on a wrapper feature selection method using entropy, least-mean squares, and linear discriminant analysis. ![]() The suggested feature list represents one of the most extensive studies in the field of financial feature engineering. 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. Stock price prediction is a challenging task, in which machine learning methods have recently been successfully used.
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