Survey of feature selection and extraction techniques for stock market prediction

作     者：Htet Htet Htun Michael Biehl Nicolai Petkov 

作者机构：Bernoulli Institute for MathematicsComputer ScienceArtificial IntelligenceUniversity of GroningenGroningenThe Netherlands 

出 版 物：《Financial Innovation》 (金融创新（英文）)

年 卷 期：2023年第9卷第1期

页      面：667-691页

核心收录：

学科分类：12[管理学] 02[经济学] 0202[经济学-应用经济学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 020204[经济学-金融学（含∶保险学）] 

基　　金：funded by The University of Groningen and Prospect Burma organization 

主　　题：Feature selection Feature extraction Dimensionality reduction Stock market forecasting Machine learning 

摘      要：In stock market forecasting,the identification of critical features that affect the performance of machine learning(ML)models is crucial to achieve accurate stock price *** review papers in the literature have focused on various ML,statistical,and deep learning-based methods used in stock market ***,no survey study has explored feature selection and extraction techniques for stock market *** survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market *** conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–*** review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the *** also describe the combination of feature analysis techniques and ML methods and evaluate their ***,we present other survey articles,stock market input and output data,and analyses based on various *** find that correlation criteria,random forest,principal component analysis,and autoencoder are the most widely used feature selection and extraction techniques with the best prediction accuracy for various stock market applications.