Mid-price prediction based on machine learning methods with technical and quantitative indicators
Open Access
- 12 June 2020
- journal article
- research article
- Published by Public Library of Science (PLoS) in PLOS ONE
- Vol. 15 (6), e0234107
- https://doi.org/10.1371/journal.pone.0234107
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.Funding Information
- MSCA-ITN-ETN (675044)
This publication has 36 references indexed in Scilit:
- High-Frequency Technical Trading: The Importance of SpeedSSRN Electronic Journal, 2012
- Prieurianin Causes Weight Loss in Diet-Induced Obese Mice and Inhibits Adipogenesis in Cultured PreadipocytesJournal of Diabetes & Metabolism, 2010
- Feature selection with dynamic mutual informationPattern Recognition, 2009
- Multivariate Density Forecast Evaluation and Calibration In Financial Risk Management: High-Frequency Returns on Foreign ExchangeThe Review of Economics and Statistics, 1999
- On Portfolio Optimization: Forecasting Covariances and Choosing the Risk ModelThe Review of Financial Studies, 1999
- Time Series Analysis, Forecasting, and ControlTechnometrics, 1995
- Using mutual information for selecting features in supervised neural net learningIEEE Transactions on Neural Networks, 1994
- Identification of rational transfer function from frequency response sampleIEEE Transactions on Aerospace and Electronic Systems, 1990
- Mean reversion in stock prices: Evidence and ImplicationsJournal of Financial Economics, 1988
- Co-Integration and Error Correction: Representation, Estimation, and TestingEconometrica, 1987