# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Descriptions
#### 4.1.3 Lstm
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference No.** & **Author** & **Dataset** & **Variables** & **Model** \\ \hline
[48] & [PERSON] & China’s A-share market &
\begin{tabular}{c} The open price, close price, highest \\ price, lowest price, and trading \\ volume and I1 indicators \\ \end{tabular} & RNN with LSTM \\
[49] & [PERSON]. &
\begin{tabular}{c} Facebook \\ stocks, Google stocks, and Bitcoin \\ stocks collected from Yahoo finance \\ \end{tabular} & Close price & RNN with LSTM \\
[50] & [PERSON] & \begin{tabular}{c} SSC90 index \\ Png An Bank \\ \end{tabular} & \
\begin{tabular}{c} N/A \\ Close price \\ \end{tabular} &
\begin{tabular}{c} LSTM \\ Kmeans-LSTM \\ \end{tabular} \\
[51] & [PERSON]. &
\begin{tabular}{c} Ping An Bank \\ SGE Composite Index, SSE 50, CSI \\ Composite Index \\ \end{tabular} & Close price & Time-weighted LSTM \\
[52] & [PERSON]. & \
\begin{tabular}{c} 500, CSI 300, and S2 SE \\ Composite Index \\ \end{tabular} &
\begin{tabular}{c} Open price, close price, high price, \\ low price, and volume exponentially \\ weighted moving averages, etc. (175 \\ \end{tabular} \\
[53] & [PERSON], [PERSON] & \begin{tabular}{c} I. Standard8/Poor’s 500 index \\ \end{tabular} & \
\begin{tabular}{c} 1. Financial domain news text \\ 2. Close price \\ \end{tabular} &
\begin{tabular}{c} LSTM \\ 2. Close price \\ \end{tabular} \\
[54] & dos Santos Pinheiro & \
\begin{tabular}{c} Expert recommendation from \\ TipRanks company \\ \end{tabular} &
\begin{tabular}{c} A stock identifier, a list of expert \\ recommendations of varying length, \\ and optional target labels (class) \\ \end{tabular} \\
[55] & [PERSON]. & \
\begin{tabular}{c} Morning edition of the Nikkei \\ newspaper \\ Nikkei 25 \\ China stock market in Shanghai and \\ Shenzhen from Yahoo finance \\ \end{tabular} &
\begin{tabular}{c} I. Paragraph Vector \\ 2. Open, close, highest, and \\ lowest price \\ \end{tabular} \\
[56] & [PERSON] & \begin{tabular}{c} China stock market in Shanghai and \\ Shenzhen from Yahoo finance \\ \end{tabular} & \
\begin{tabular}{c} Volume, high, low, open, close price \\ Technical indicators \\ \end{tabular} &
[57] & [PERSON], [PERSON] and [PERSON] & \begin{tabular}{c} Forest Exchange rate \\ \end{tabular} & \
\begin{tabular}{c} LSTM \\ stock data, stock news, capital stock \\ and shareholders, and financial \\ analysis \\ \end{tabular} &
\begin{tabular}{c} DeepLSTM with encoder \\ LSTM with ranking relation \\ LSTM with GA \\ LSTM with [PERSON] \\ \end{tabular} \\
[58] & [PERSON], [PERSON] & Chinese stock & \
\begin{tabular}{c} Open, high, low, closing, volume, \\ and technical indicators \\ \end{tabular} &
\begin{tabular}{c} LSTM with GA \\ LSTM with [PERSON] \\ \end{tabular} \\
[59] & [PERSON], [PERSON], et al. & \begin{tabular}{c} NASDAQ and NYSE \\ [PERSON]. \\ \end{tabular} & \
\begin{tabular}{c} Stock data \\ Corex Stock Price Index \\ \end{tabular} &
\begin{tabular}{c} LSTM with ranking relation \\ LSTM with GA \\ LSTM with GA \\ LSTM with [PERSON] \\ \end{tabular} \\
[60] & [PERSON], [PERSON], et al. & \begin{tabular}{c} Chinese Stock Market \\ SGP 500 Index, Shanghai Composite \\ Index, IBM from NYSE, MSFI from \\ NASDAQ and PingAn Insurance \\ Company of China (PAIC) \\ \end{tabular} & \
\begin{tabular}{c} Open, high, low, closing, volume, \\ and technical indicators \\ \end{tabular} &
\begin{tabular}{c} LSTM with Generative \\ Adversarial Network (GAN) \\ \end{tabular} \\
[61] & [PERSON], [PERSON], et al. & Apple stock price & Sentiment and stock price data & LSTM with sentiment analysis model \\
[63] & [PERSON], [PERSON], et al. & Chinese Stock market & \
\begin{tabular}{c} Market information including \\ transaction records \\ \end{tabular} &
\begin{tabular}{c} LSTM with CNN \\ LSTM. It was found that finance tweets that were posted from market closure to market \\ open the next day had more predictive power on the next day’s stock movement. The weighted sentiment on the max follower on StockTwits also performed much better than other methods [34]. \\ [PERSON], [PERSON] proposed an LSTM model combined with crude oil price, gold price, and moving average, which performed much better than the LSTM model without them and the SVM model. It showed that the crude oil and gold prices had some impact on stock price prediction [35]. \\ [PERSON] proposed an LSTM model that outperformed the LR and SVR models. He also compared the different activation functions with different optimizers and concluded that the tanh activation with the Adam algorithm performs best with an accuracy of 98.49% [36]. \\ \hline \hline \end{tabular}
\end{table}