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Table 1 Analysis based on types of features, feature selection/extraction techniques, predictive models, and datasets

From: Survey of feature selection and extraction techniques for stock market prediction

Study

Types of features

Feature selection/extraction techniques

Prediction methods

Datasets

1. Haq et al. (2021)

Basic features, Technical indicators

LR, SVM, RF

Deep generative model

88 stocks from NASDAQ

2. Labiad et al. (2016)

Technical indicators

RF

Gradient boosted trees (GBT), SVM, RF

Moroccan stock market

3. Rana et al. (2019)

Basic features

Decision tree classifier, Extra Tree classifier

LR, SVR, LSTM

Spanish stock market

4. Aloraini (2015)

Open prices

Pearson correlation coefficient (PCC), Spearman correlation, Euclidean distance, Manhattan distance, Search AIC score

Lasso estimate

11 equities in Saudi stock market

5. Kumar et al. (2016)

Basic features, Technical indicators

Pearson correlation, Spearman correlation, Relief algorithm, Random forest (RF)

PSVM

12 stock indices from different international markets

6. Alsubaie et al. (2019)

Technical indicators

Gain ratio, Relief algorithm, Correlation, Cost-based Naive Bayesian, Accuracy-based Naive Bayesian

9 different classifiers

99 stocks and TASI market in-dex

7. Li et al. (2022)

Technical indicators Fundamental indica- tors

PCC

Broad learning system

4 stocks from Shanghai Stock Exchange

8. Nabi et al. (2019)

Basic features

9 different methods

15 different classifiers

10 stocks from NASDAQ

9. Yuan et al. (2020)

Technical indicators, Fundamental indica-tors

RFE, RF

SVM

RF

ANN

Chinese A-share stocks

10. Botunac et al. (2020)

Basic features, Technical indicators

RFE, Linear regression, Decision Tree, RF

LSTM

Apple, Microsoft, Facebook

11. Shen et al. (2020)

Technical indicators

RFE

PCA

LSTM

3558 Chinese stocks

12. Chen et al. (2017)

Technical indicators

Information gain

SVM

Chinese stock market indices

13. Sun et al. (2019)

Technical indicators

FSMRMR, CMIM

ARMA-GARCH-NN

US stock market

14. Singh et al. (2021)

Technical indicators, Fundamental indica-tors

PCA

6 different classifiers

505 stocks from S& P 500

15. Ampomah et al. (2020)

Basic features, Technical indicators

PCA

6 tree-based Classifiers

8 stocks from NYSE, NASDAQ, NSE

16. Siddique et al. (2019)

Basic features

PCA

SVR

TATA motors stock index

17. Iacomin (2015)

Technical indicators

PCA

GA

SVM

16 Forex stocks from Bloomberg

18. Cai et al. (2012)

Basic features, Technical indicators

RBM

SVM

S& P 500 index

19. Das et al. (2019)

Technical indicators

PCA, Factor analysis (FA), Firefly optimization (FO), Genetic algorithm (GA), FO with GA

ELM, OSELM, RBPNN

4 different stock market indices

20. Qolipour et al. (2021)

Basic features, Technical indicators

PCA

Decision tree, RF, Gradient boosted tree (GBT)

2 stocks from Tehran stock exchange

21. Ampomah et al. (2021)

Technical indicators

PCA, LDA, FA

Gaussian Naïve Bayes (GNB)

7 stocks from NYSE, NASDAQ, NSE

22. Chen et al. (2020)

Basic features, Technical indicators

Information gain

FW-SVM

30 stocks

23. Gunduz etal. (2017)

Technical indicators

Gain ratio Relief algorithm

Gradient boosting ma-chine (GBM)

3 stocks in BIST market index

24. Kumar et al. (2021b)

Basic features, Technical indicators

PCA

ANN

3 stock indices

25. Tang et al. (2018)

historical relative re-turns

PCA

KNN

CSI 300 index

26. Barak et al. (2017)

Fundamental indica-tors

GA

Multiple classifiers

400 stocks

27. Farahani et al. (2021)

Technical indicators

GA

ANN

5 stock indices

28. Chong et al. (2017)

10 lagged returns

Autoencoder

DNN

38 stocks

29. Bhanja et al. (2022)

Technical indicators

Autoencoder

5 ML classifiers

2 market indices

30. Xie et al. (2021)

Fundamental indica-tors

Autoencoder

SVM

5 market indices

31. Dami et al. (2021)

Basic features

Autoencoder

LSTM

10 stocks

32. Gunduz (2021)

Technical indicators

Autoencoder

SVM

LSTM

8 stocks