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Review
. 2023;9(1):26.
doi: 10.1186/s40854-022-00441-7. Epub 2023 Jan 12.

Survey of feature selection and extraction techniques for stock market prediction

Htet Htet Htun  1 , Michael Biehl  1 , Nicolai Petkov  1
Affiliations
Review

Survey of feature selection and extraction techniques for stock market prediction

Htet Htet Htun et al. Financ Innov. 2023.

Abstract

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.

Keywords: Dimensionality reduction; Feature extraction; Feature selection; Machine learning; Stock market forecasting.

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Conflict of interest statement

Competing interestsThe authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Phases of the stock market prediction with feature selection and ML method combination
Fig. 2
Fig. 2
Flow diagram of the selected articles
Fig. 3
Fig. 3
Analysis based on publication years
See this image and copyright information in PMC

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