# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Descriptions
#### 4.1.6 Other Deep Learning Methods  
Eight articles that used other deep learning technology for stock and Forex prediction are briefly described below. Table 7 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
[84] & [PERSON] & CSI300 & Open price, high price, low price, close price, and volume & MNFF \\
[85] & [PERSON] & ANUE & Stock messages as information to form & \\  & & & the text feature of each stock news (title, summary, and keywords) & \\  & & & & Open price, high price, low price, close price, volume, MACD, CCI, MRO, Dollar, & \\
[86] & [PERSON] & Finncal, CTBC HOLDINGS, & EMA/2/0, MAs, MAMAMG, MROMI2, & \\  & & ESFH, and FFHC & ROC, ISI, WAD, exchange rate, and & \\  & & & & interest rate & \\
[87] & [PERSON] & S\&P 500, VN-index, & Open price, high price, low price, close price, volume, stochastic oscillator, & \\  & & & Bloomberg, Reuters & William (\%R), and ISI & labeling-Stock2 Vec \\
[88] & [PERSON] & Financial Times Stock; Exchange & Processed news article & embedding-BCRU \\  & & & 100 index (FTS 100) & Candesick charts (images rather than & Convolutional \\  & & & & \\
[89] & [PERSON] & Chinese stock price; News (not & 1. Close price and volume & \\  & & & given specific data) & 2. News corpus sequence & \\  & & & Nine Dow Jones companies & \\  & & & representing each sector; & \\  & & & & \\  & & & & \\
[90] & [PERSON], [PERSON] and [PERSON] & & (FDM), consumer services (FG), & \\  & & & technology (AAPL), healthcare & \\  & & & (UNH), consumer goods (WMT), & \\  & & oil and gas (XOM), basic materials & \\  & & & (DD), and telecommunications & \\  & & & (V2) From Yahoo & \\  & & & & \\
[91] & [PERSON], and Khushi & Forex exchange rates & & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\
[92] & [PERSON], [PERSON], et al. & Apple Inc. and S\&P 500 & Trend indicators: moving average, & \\  & & & & \\  & & & & \\  & & & \\  & & & & \\  & & & \\  & & & & \\ \end{tabular}
\end{table} 
Table 7: Information on the papers which used other deep learning methods. [PERSON] proposed a novel end-to-end model named multi-filters neural network (MFNN) specifically for feature extraction on financial time-series samples and price movement prediction task. Both convolutional and recurrent neurons were integrated to build the multi-filters structure. The results showed that the proposed model is better than the RNN, CNN, and traditional machine learning methods [84]. [PERSON] proposed a keyword-based attention network into Hierarchical Attention Networks (HAN), namely the HKAN model, to learn the relationships between dimensional sentiments (trend and trading) and stock messages and outperformed the HAN network [85]. [PERSON] proposed three different models to predict the price of the stock: LSTM, Seq2 seq, and Wavenet. According to his experiments, Wavenet outperformed the other two models [86]. [PERSON] proposed a network to predict the directions of stock prices by using both financial news and sentiment dictionary. His results showed that Two-stram gated recurrent unit (TGRU) achieved better performance than GRU and LSTM and that Stock2 Vec is more efficient in dealing with financial datasets [87]. [PERSON] proposed a Convolutional AutoEncoder model to learn a stock representation and converted a 4-channel stock time series (lowest, highest, open, and close price for the day) to candlestick charts by using the synthesis technique to present price history as images. This method successfully avoided expensive annotation. The proposed model outperformed the FTSE 100 index and many well-known funds in terms of total return [88]. [PERSON] proposed a Hybrid Attention Networks (HAN) to predict the stock trend based on the sequence of recent related news. Moreover, he applied the self-paced learning (SPL) mechanism to achieve effective and efficient learning.