Table 3.
Summary of the existing DL-based stock price forecasting approaches.
| Reference No. | Method | Comparisons | Dataset | Targets | Input features | Metrics | Results |
|---|---|---|---|---|---|---|---|
| 50 | CNN | LR, CNN-Rand, CNN-Corr, LR With FS | BIST 100 Index | Hourly stock price direction | 25 technical indicators with different time lags | Macro-Averaged F-Measure | CNN-Corr classifier yielded the best performance |
| 51 | CNN + frequent patterns | ARIMA, Wavelet + ARIMA, HMM, LSTM, SFM | S&P 500 and 07 individual stocks | Trend of stock price | Closed value | Accuracy, recall, precision, f1-score | Proposed method outperformed the others with a 4%–7% accuracy improvement |
| 55 | LSTM | Random Forest | S&P 500 | Directional movements of stock price | Adjusted closing prices and opening prices | Various metric (mean, std error, sharpe ratio, …) | LSTM outperforms random forests |
| 56 | LSTM | LASSO-LSTM, PCA-LSTM, LASSO-GRU, PCA-GRU | Shanghai Composite Index | Stock price trend | Open, high, low, trading volume, and other technical indicators | RMSE, MAE | LSTM and GRU with LASSO yielded better accuracy than models with PCA |
| 62 | BiLSTM | WAE-BLSTM, W-BLSTM, W-LSTM, BLSTM, LSTM | S&P500 | Next day closing price | Open, high, low, close (OHLC), 08 technical indicators | MAE, RMSE, R2 | WAE-BLSTM model outperformed the other models. MAE (0.0211), RMSE (0.0272), and R² (0.8934) |
| 64 | AE-BiLSTM-ECA | CNN, LSTM, BiLSTM, CNN-LSTM, AE-LSTM, CNN-BiLSTM, AE-BiLSTM, BiLSTM-ECA, AE-LSTM-ECA, CNN-LSTM-ECA | Shanghai Stock Composite Index (SSCI) and CSI 300 | Closing price | Seven characteristics such as closing, high, open, low, previous day's closing price, up or down amount and and up or down rate |
MSE, RMSE, MAE, MAPE |
AE-BiLSTM-ECA obtain the best accuracy. CSI 300 stock data: MSE: 3158.452 RMSE: 56.200 MAE: 36.681 MAPE: 1.020 SSCI stock data MSE: 1935.398 RMSE: 43.993 MAE: 28.940 MAPE: 1.019 |
ARIMA: autoregressive integrated moving average; BiLSTM: bidirectional long short-term memory; LR: logistic regression; GRU: gated recurrent unit; PCA: principal component analysis; AE: auto-encoder; ECA: efficient channel attention; MAPE: mean absolute percent error; MSE: mean squared error; RMSE: root mean squared error; MAE: mean absolute error; DL: deep learning.