# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Descriptions
#### 4.1.3 Lstm

Table 4: \(Cont.\)[PERSON] proposed an RNN with the LSTM model. The model had the ability to keep the memory of historical stock returns in order to forecast future stock return output. It was worth noting that recent stock information rather than old, related stock information was stored and used. The network also outperformed the Feed Forward ANN model [37]. [PERSON], [PERSON] proposed a dynamic LSTM model. The results showed that stock prediction accuracy based on Mean absolute error (MAE), Mean absolute percentage error (MAPE), Root Mean Square Error (RMSE), and Mean square error (MSE) obtained by the dynamic LSTM model were much better than that by the static LSTM model. The dynamic model also consistently outperformed the linear models SA-5 and EMA-0.5 when predicting four stocks [38]. [PERSON], [PERSON] proposed an LSTM model which used the average of the previous five days' stock market information (open, high, low, volume, and close) as the input value. The initial prediction was calculated using this value. The prediction was then used as part of the average of the stock price information for the next five days through the ARIMA method. Moreover, he utilized technical analysis indicators to consider whether to buy stocks, or to continue to hold stocks or to sell stocks [39]. [PERSON], [PERSON] proposed an LSTM model followed by Gated recurrent unit (GRU). Both LSTM and GRU were powerful recurrent networks that could perform better and faster in terms of accuracy in regression-based prediction. The proposed model outperformed the LSTM only, GRU only, and GRU followed by LSTM models [40]. [PERSON], [PERSON] proposed a novel data augmentation approach for stock market index forecasting through the ModAugNet framework, which consisted of two modules: an overfitting prevention LSTM module and a prediction LSTM module. The overfitting problems were mainly caused by the limited availability of data points for training [41]. [PERSON], [PERSON] proposed some LSTM models to forecast stock price volatility that combined the LSTM model with various generalized autoregressive conditional heteroscedasticity (GARCH)-type models. He found that the Geneva Emotion Wheel (GEW)-LSTM model, which combined all three models, GARCH, exponential generalized autoregressive conditional heteroscedasticity (EGARCH), and exponential weighted moving average (EWMA), with LSTM, performed best [42]. [PERSON] proposed an improved MI-LSTM based on LSTM and attention mechanism, which achieved better performance in extracting potential information and filtering noise. The model could assign different weights to different input series to keep the dominant status of the mainstream while absorbing information from leaky input gates [43]. [PERSON] proposed an attention-based LSTM model that could solve the problem of exploding and vanishing gradients and thus did not effectively capture long-term dependencies [44]. [PERSON] proposed an LSTM model which was compared with the DNN model. The proposed model was able to predict volatile movements in the true data. In general, it was able to recognize the directionality of the changes in the true data more accurately than the DNN [45]. [PERSON] proposed an LSTM model to predict the price of the top 10 industries included in Taiwan Stock Exchange Corporation (TWSE). In the experimental results, most of the results were reasonable except for the Midland Holdings Limited (MTK) stock [46]. [PERSON] proposed a linear Autoregressive Integrated Moving Average (ARIMA) model and LSTM network for each series to produce next-day predictions. Wavelet methods decomposed a series into approximation and detail components to better explain behavior over time. He combined these techniques in a novel ensemble model in an attempt to increase forecast accuracy [47]. [PERSON] proposed a simple but efficient method to predict future stock return ranking without handcrafted features [48]. [PERSON] proposed an approach to predict stock market ratios using artificial neural networks.