Survey of feature selection and extraction techniques for stock market prediction

Financial Innovation

Review

Htet Htet Htun¹  Michael Biehl¹  Nicolai Petkov¹ 

[1] Bernoulli Institute for Mathematics, Computer Science, Artificial Intelligence, University of Groningen, Groningen, The Netherlands;

关键词: Feature selection;    Feature extraction;    Dimensionality reduction;    Stock market forecasting;    Machine learning;   

DOI  :  10.1186/s40854-022-00441-7

 received in 2022-04-19, accepted in 2022-12-13,  发布年份 2022

来源: Springer

【 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 predictions. Several review papers in the literature have focused on various ML, statistical, and deep learning-based methods used in stock market forecasting. However, no survey study has explored feature selection and extraction techniques for stock market forecasting. This survey presents a detailed analysis of 32 research works that use a combination of feature study and ML approaches in various stock market applications. We conduct a systematic search for articles in the Scopus and Web of Science databases for the years 2011–2022. We review a variety of feature selection and feature extraction approaches that have been successfully applied in the stock market analyses presented in the articles. We also describe the combination of feature analysis techniques and ML methods and evaluate their performance. Moreover, we present other survey articles, stock market input and output data, and analyses based on various factors. We 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.

【 授权许可】

CC BY   
© The Author(s) 2023

【 图 表 】

41116_2022_35_Article_IEq574.gif

Fig. 42

41116_2022_35_Article_IEq567.gif