Table 4.

Summary of the existing hybrid-based stock price forecasting approaches.

Reference No.	Method	Comparisons	Dataset	Targets	Input features	Metrics	Results
Hybrid traditional approaches
65	SVM-KNN	CEFLANN, FLIT2FNS	Bombay Stock Exchange (BSE Sensex) and CNX Nifty	Trends, volatility, and momentum of stock indices	Open, low, high, closing, and technical indicators	MAPE, MSFE, RMSFE	SVM-KNN has better performance than CEFLANN and FLIT2FNS
66	SVR-TLBO	OFS-SVR-TLBO, KPCA-SVR-TLBO	Tata Steel from Bombay Stock Exchange	Closing price	07 features	MAE, RMSE, MAPE	KPCA-SVR-TLBO performed better than OFS-SVR-TLBO
Hyrid deep learning with traditional approaches
71	TI-CNN	CNN-TA, 1D CNN, CNN-LSTM	NASDAQ, NYSE	Stock movement, Buying and selling points	10 technical indicators	Accuracy, f1-score	TI-CNN achieves high prediction accuracy
72	CNN + optimizing algorithm	CNN, RS-CNN, FF-CNN, PSO-CNN	Tata Motors from Yahoo Finance	Closing price	date, open, low, close, high, volume, and adjacent close	MSE, MAE, RMSE, MAPE	FF-CNN outperformced the others
Hybrid deep learning approaches
76	CNN-LSTM	MLP, CNN, RNN, LSTM, CNN-RNN	Shanghai Composite Index	Next day closing price	Open, high, low, closing price, volume, turnover, ups and downs, and change of the stock data	MAE, RMSE, R2	CNN-LSTM obtained the best performance MAE(27.564), RMSE(39.688), R2(0.9646)
77	SACLSTM	SVM, CNN-cor, CNNpred, ANN	10 stocks from American market and Taiwan	Direction of the stock market (rise and fall)	Historical data, futures, and options	Accuracy	SACLSTM performs relatively well compared to the others
78	BiSLSTM	MLP, RNN, LSTM, BiLSTM, CNN-LSTM, CNN-BiLSTM	Shenzhen Component Index	Closing price	Open, high, low, closing, volume, turnover, ups and downs, and change	MAE, RMSE, R2	CNN-BiSLSTM has optimal values for MAE (113.47137), RMSE (162.53164), R2 (0.98634)
81	CNN-BiLSTM-AM	MLP, CNN, RNN, LSTM, BiLSTM, CNN-LSTM, CNN- BiLSTM, BiLSTM-AM	Shanghai Composite Index	Next day stock closing price	Open, high, low, closing, volume, turnover, ups and downs, and change	MAE, RMSE, R2	CNN-BiLSTM-AM yielded the best results MAE(21.952), RMSE(31.694), R2(0.9804)
82	CNN-BiLSTM-ECA	CNN, LSTM, BiLSTM, CNN-LSTM, CNN-BiLSTM, BiLSTM-ECA, CNN-LSTM-ECA, CNN-BiLSTM-ECA	Shanghai Composite Index, China Unicom, CSI 300	Next day closing price	Closing, high, low, open, previous day's closing price, change, ups and downs, and other time series data	MSE, RMSE, MAE	CNN-BiLSTM-ECA obtained the best performance
85	BiLSTM-MTRAN-CNN	BiLSTM-SA-TCN, CNN-BiLSTM, CNN-BiLSTM-AM, BiLSTM	A-share Index, Shanghai Composite Index, Shenzhen Component Index, CSI 300 and Growth Enterprise Board Index	Next day closing price	Trading data and technical indexes data	MAE, MSE, RMSE, R2	BiLSTM-MTRAN-TCN outperforms the other methods
86	FDG-Trans	DeepLOB, DeepAtt, MHF	Limit order book (LOB), CSI-300	Price movements.	LOB information	R2, MSE, MAE	The FDG-Trans has less error compared to the other models
87	WGAN-S	H-LSTM, GAN, GAN-S, LSGAN, LSGAN-S, WGAN	Taiwan Stock Exchange Capitalization Weighted Stock Index	Three trading actions: buying, selling, and holding	Opening, closing, highest, lowest, trading volume, and technical indices	Cumulative return on investment, the Sharpe ratio, and winning percentage.	GAN outperform LSTM
88	DCGAN	ARIMAX-SVR, RF regressor, LSTM, GAN	FTSE MIB Index	Closing price	Technical indicators	RMSE, MAE, MAPE	DCGAN obtained the best performance
89	Improve GCNN	MOM, MR, LSTM, DARNN, SFM, GCN, TGC, HATS, STHGCN	A-share market stock prices in China	Trend of stock price	Open, high, low, close, and trading volume	Accuracy, recall, precision, f1-score, AUC	Proposed model achieves the best accuracy,
90	CT-GCNN	GNN, LSTM, RNN, CNN, BP	A-share market stock prices in China	Stock price movement	Open, close, exchange rate, high, low, trading volume	MSE, MAPE	CT-GCNN model demonstrated stability and superiority

CT-GCNN: conceptual-temporal graph convolutional neural network; BiLSTM: bidirectional long short-term memory; KNN: k-nearest neighbor; SVM: support vector machine; SVR: support vector regression; RF: random forest; RNN: recurrent neural network; KPCA: Kernel principal component analysis; OFS: orthogonal forward selection; teaching-learning-based optimization; FF: firefly algorithm; PSO: particle swarm optimization; RS: random search; ECA: efficient channel attention; DCGAN: deep convolutional generative adversarial network; GAN: generative adversarial network; MAPE: mean absolute percent error; MSE: mean squared error; RMSE: root mean square error; MAE: mean absolute error; AUC: area under the receiver operating characteristic curve