ReviewFinancial innovation2023
Survey of feature selection and extraction techniques for stock market prediction.
Review in Financial innovation, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Integrating macroeconomic and technical indicators into forecasting the stock market: a hybrid approach for efficient feature selection.Scientific reports · 2026Article
- Information-Theoretic Causal Feature Selection via Markov Blanket Discovery for Stock Return Direction Prediction: Evidence from China.Entropy (Basel, Switzerland) · 2026Article
- Hybrid CNN-LSTM-GNN Neural Network for A-Share Stock Prediction.Entropy (Basel, Switzerland) · 2025Article
- Article
- A Novel Improvement of Feature Selection for Dynamic Hand Gesture Identification Based on Double Machine Learning.Sensors (Basel, Switzerland) · 2025Article
- A robust and interpretable ensemble machine learning model for predicting healthcare insurance fraud.Scientific reports · 2025Article
- Multi-Strategy Enhanced Parrot Optimizer: Global Optimization and Feature Selection.Biomimetics (Basel, Switzerland) · 2024Article
- Developing an Early Warning System for Financial Networks: An Explainable Machine Learning Approach.Entropy (Basel, Switzerland) · 2024Article
- Enhanced stock market forecasting using dandelion optimization-driven 3D-CNN-GRU classification.Scientific reports · 2024Article
- The contagion effect of heterogeneous investor groups.PloS one · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.