# A Survey of Forex and Stock Price Prediction Using Deep Learning
## 4 Results
### Papers Descriptions
#### 4.1.4 Dnn  
Thirteen articles that used DNN technology for stock and Forex prediction are briefly described below. Table 5 shows the author, variables, dataset, and model of the papers mentioned.  
[PERSON] proposed a DNN model with 715 novel input features configured on the basis of technical analysis. He also proposed a plunge filtering technique to improve the accuracy of the training model by collecting similar stocks. It is worth noting that the proposed model had great profitability [67].  
[PERSON] proposed a DNN model that used the Boruta feature selection technique to solve the problem of technical indicator feature selection and identification of the relevant technical indicators. The results showed that his model performed much better than the ANN and SVM models [68].  
[PERSON] proposed a DNN model which used Boosted approaches to predict stock market crisis episodes. According to his research, it was meaningful to know the stock market crisis to predict the price, even though his research was not specific to certain prediction methods [69].  
[PERSON] proposed a DNN network, and his results showed that DNNs generally outperform shallow neural networks and that the best networks also outperformed representative machine learning models [70].  
[PERSON] proposed a deep factor model and a shallow model with DNN. The deep factor model outperformed the linear model. This implied that the relationship between the stock returns in the financial market and the factors is nonlinear rather than linear. The deep factor model also outperformed other machine learning methods including  
\begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Reference Number** & **Author** & **Dataset** & **Variables** & **Model** \\ \hline
[67] & [PERSON] & KOSPI and KOSDAQ & 715 novel input features (including moving average and disparity of stock price) & DNN \\  & & NSE & SMA, exponential moving average, momentum indicator, stochastic oscillator, moving average & \\
[68] & [PERSON] & SSI Bank & convergrace divergence, relative strength index, & DNN \\  & & Kotak Bank & Williams R, accumulation & \\  & & Yes Bank & distribution index, and commodity channel index & \\
[69] & Chatzis, S.P. & FRED database and the SNL & Stock price, exchange rates, VIX index, gold price, TED spread, oil price, effective federal funds rate, and high yield bond returns & DNN \\  & & & 25 variables: 1. book-to-market ratio; & 2. earnings-to-price ratio; 3.  
SVR and random forest. The shallow model was superior in accuracy, while the deep model was more profitable [71].  
[PERSON] proposed DNN networks and examined the effects of three unsupervised feature extraction methods including principal component analysis, auto-encoder, and the restricted Boltzmann machine on the network's overall ability to predict future market behavior. The empirical results suggested that DNNs could extract additional information from the residuals of the auto-regressive model and could improve prediction performance; the same could not be said when the auto-regressive model is applied to the residuals of the network [72].  
[PERSON] proposed a 2-Directional 2-Dimensional Principal Component Analysis (2D\({}^{2}\)PCA) + DNN, which outperformed the RNN and (2D\({}^{2}\)PCA) + RBFNN. The paper found that the best results were generated from a window size of 20 and a dimension of 10 \(\times\) 10. The deep learning method for higher dimensions and large window sizes gave a limited performance [73].  
[PERSON] and [PERSON] proposed a novel DNN model which incorporated LSTM as well as phase-space recognition. The model had produce promising results [74].  
[PERSON], et al. proposed a DNN model with 40 nodes which showed reasonable results and appeared to be a highly profitable model [75].