# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Descriptions
#### 4.1.1 CNN
[PERSON] proposed a multi-indicator feature selection for stock index prediction based on a multichannel CNN structure without subsampling, named the MI-CNN framework. In this method, candidate indicators were selected by the maximal information coefficient feature selection (MICPS) approach to ensure the correlation with stock movements whilst reducing redundancy between different indicators [16]. [PERSON] proposed the CNN and LSTM forecasting system with financial news and historical data of the stock market. It generated seven prediction models. According to the ensemble learning method, the seven models were constructed into one ensemble model to obtain an aggregated model. Unfortunately, all models had a lower prediction accuracy [17]. [PERSON] proposed a new framework to train a DNN for stock market prediction. A new loss function was developed by adding a risk-reward function, which was derived from the trading simulation results [18]. [PERSON] proposed to incorporate a joint model using the TransE model for representation learning and a CNN, which extracted features from financial news articles. Then, he \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference No.** & **Author** & **Dataset** & **Variables** & **Model** \\ \hline \multirow{3}{*}{[21]} & \multirow{3}{*}{[PERSON]} & Chinese stocks from the SINAA & \multirow{3}{*}{Close price} & CNN-LSTM \\  & & F1 NANCE (not given & \\  & & specific data) & Close price, ISI, SMA, MACD, MFI, \\  & & & Exchange-Traded Funds (ETF) & Williams \%R, the stochastic oscillator, \\  & & & and the ultimate oscillator & \\  & & & 1. close price & \\
[23] & [PERSON] & 1. S\&P 500 index & 2. long-term, mid-term, and short-term & NTTN-CNN \\  & & & feature vectors of news headlines & \\  & & & 1. Momentum indicators: RSIS, RS110, & \\  & & & NEDA, MACD, MATLAB, & \\  & & & MACD,Spiral, Slowk, Slowed, Fast, & \\
[24] & [PERSON], [PERSON] and Khushi & USDIFY exchange rate & Fast, WS, WIR, WIR, ROCS, ROC1, & \\  & & & R002 CB, CCG, CCG, CCG, and CCG20 & \\  & & & 2. Volume indicators: ATRS, ATR10, & \\  & & & AT280, NATFS, NATR10, NATR20, & \\  & & & and TRANCE & \\
[3] & [PERSON] and [PERSON] & Chinese stock market & \multirow{3}{*}{Closing price} & CNN \\
[25] & [PERSON], [PERSON] & Taiwan index futures & \multirow{3}{*}{Open, high, low, and closing price} & GAF + CNN \\
[26] & [PERSON] & & S\&P500 & Open, high, low, close, adjusted close, \\  & & & and volume. & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._combined the model with LSTM to predict the price movement. The model could improve the accuracy of text feature extraction while reducing the sparseness of news headlines [19]. [PERSON], [PERSON] used CNN, LSTM, and RNN architectures to forecast the price of NSE-listed companies and compared their performance. The final results showed that CNN is the best architecture to predict the price of stock because it could identify the trend of the directional change [20]. [PERSON] proposed a CNN-LSTM model, and the model performed a basic momentum strategy and benchmark model for which the return rates were 0.882 and 1.136, respectively. The CNN part could extract useful features even from low signal-to-noise time-series data, and the LSTM part could predict future stock prices with high accuracy. Then, the predicting outcomes were used as timing signals [21]. [PERSON] proposed a 2D-CNN model. This model adopted a sliding window approach and then generated images by taking snapshots that are bounded by the window over a daily period. The model could predict the next day's prices with 72% accuracy and ended up with 5:1 of the initial capital [22]. [PERSON] proposed a deep learning method for the event-driven stock market prediction. Firstly, events were extracted from news text and represented as dense vectors, trained using a novel neural tensor Network (NTN). Secondly, CNN was used to model both the short-term and long-term influences of events on stock price movements [23]. [PERSON] proposed a Wavelet Denoised-ResNet CNN for Forex exchange rate prediction.