# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results  
### Papers Descriptions  
#### 4.1.1 CNN  
Seventeen articles that used CNN technology for stock and Forex prediction are briefly described below. Table 2 shows the author, variables, dataset, and model of the papers mentioned. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No.** & **Author** & **Dataset** & **Variables** & **Model** \\ \hline \multirow{4}{*}{[11]} & \multirow{4}{*}{[PERSON], [PERSON]} & 1, Top 4 performing companies of US, Hong & 1. Open price, & \multirow{4}{*}{CNN} \\  & & 2. Twitter dataset & & high price, low price, AdjClose price, & \\  & & 1. News collected on a & & volume, and close price & \\  & & 2. Twitter dataset & & Sentiment (positive, neutral, and \\  & & 1. News collected on a & & negative sentiment). & \\  & & & financial website. & Adjacency matrix calculated by the & \\  & & 2. 30 stocks of fortune 500 correlation & & correlation & \\  & & companies, such as WMT, XOM, and AAPL & & coefficient and using news co-mentions & \\
[13] & Sim, HS, SBF 500 min data & & & \\  & & 5 kF 500, NASDAQ, Dow Jones & & & \\  & & & industrial average, NYSE, & & \\  & & & and Russell & & \\  & & & & \\  & & & Standard and Poor’s (S\&P) 500 & & CNN-bidirectional \\
[15] & [PERSON], [PERSON] & & stock dataset & & \\  & & 16 & [PERSON] & \& SP 500 Index ET (SPY) & High, low and close price; volume, RSI, \\  & & & & KD, WR, ROC, and CCI & \\
[17] & [PERSON] & 1. Crawling financial news & 1. word vector, baseline and keyword & \\  & & 2. Baidu Index & & training set in the news & CNN-1 STM \\  & & & Reuters and Reddit & 2. Close price & \\  & & & Standard \& Poor’s 500 Index & Word vectors of headlinesClose prices, & \\  & & (S\&P\&P)00 and Dow Jones & Bollinger band, RSI, and stochastic & \\  & & & Industrial Average (DJIA) & & oscillator \\  & & & & 1. Financial news corpus with & \\  & & & headlines from Apple & & \\
[19] & [PERSON] & 1. Thomson Reuters & 2. Open price: close price; high price; & \\  & & 2. Standard \& Poor’s 500 index & low price; volume; stochastic oscillator & \\  & & (S\&P\&P)00) & (\%K), [PERSON] (LM) \%R indicator; & \\  & & & and Relative Strength & & Index (RSI) & \\
[20] & [PERSON] & \multicolumn{2}{c}{NSE listed companies: Infosys, TCS, and CIPLA} & Close price & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Information on the papers which used the Convolutional neural network (CNN) model. Figure 3: The systematic literature review in this paper. [PERSON] proposed a CNN model that made use of the price and sentiment analysis as input and compared the proposed model with linear regression and Support vector machine (SVM). He concluded that not all significant events have a serious impact on stock exchange prediction. However, more important local events could affect the performance of prediction algorithms [11]. [PERSON] proposed a new network using graph theory and CNN, which leveraged spatiotemporal relationship information between different stocks by modelling the stock market as a complex network. Meanwhile, the model used both stock indicators and financial news as input [12]. [PERSON] proposed a CNN network that uses 9 technical indicators (close price, Simple moving average (SMA), Exponential moving average (EMA), Rate of change (ROC). Moving average convergence/divergence (MACD), Fast%K, Slow%D, upper band, and lower band) to verify the applicability of the CNN method in the stock market. He concluded that the use of technical indicators in stock price forecasting by CNN has no positive effect [13]. [PERSON] proposed two models: 2D-CNN and 3D-CNN using 82 different technical indicators. These two structures could improve the predictive performance of the baseline algorithm by about 3-11% [14]. [PERSON] proposed a model that had multiple pipelines of CNN and bidirectional LSTM units. It could improve prediction performance by 9% using a single pipeline deep learning model and by over a factor of six using support vector machine regressor model on the S&P 500 grand challenge dataset [15].