# A Survey of Forex and Stock Price Prediction Using Deep Learning
###### Abstract  
Predictions of stock and foreign exchange (Forex) have always been a hot and profitable area of study. Deep learning applications have been proven to yield better accuracy and return in the field of financial prediction and forecasting. In this survey, we selected papers from the Digital Bibliography & Library Project (DBLP) database for comparison and analysis. We classified papers according to different deep learning methods, which included Convolutional neural network (CNN); Long Short-Term Memory (LSTM); Deep neural network (DNN); Recurrent Neural Network (RNN); Reinforcement Learning: and other deep learning methods such as Hybrid Attention Networks (HAN), self-paced learning mechanism (NLP), and Wavenet. Furthermore, this paper reviews the dataset, variable, model, and results of each article. The survey used presents the results through the most used performance metrics: Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Mean Square Error (MSE), accuracy, Sharpe ratio, and return rate. We identified that recent models combining LSTM with other methods, for example, DNN, are widely researched. Reinforcement learning and other deep learning methods yielded great returns and performances. We conclude that, in recent years, the trend of using deep-learning-based methods for financial modeling is rising exponentially. deep learning; stock; foreign exchange; financial prediction; survey +
Footnote †: journal: Applied System  
Continuous development in the AI field leads to the wide use of deep learning techniques in many research fields and practical scenarios. Applications include natural language processing, image recognition, medical predictions, and more. The neural networks used in these applications have also developed and improved due to the rise of deep learning. For example, reinforcement learning has gained popularity since AlphaGo defeated the best chess player at the time by using it, and reinforcement learning has been implemented in the financial prediction field since then [3]. These technological breakthroughs have given the stock and Forex prediction models a solid foundation to start and greater room to improve. The highly complex nonlinear relationship of deep learning can fully describe the complex characteristics of the influencing factors. Many other fields have verified the accuracy of a deep learning model for prediction accuracy, such as image classification and gene analysis. Research results are also obtained for time-series data analysis and prediction with a deep learning algorithm; for example, deep learning is used to predict offline store traffic [4]. Overall, deep learning models have excellent performances in other research fields. Therefore, it is feasible to predict stock and Forex trends with deep learning. Financial researchers around the world have been studying and analyzing the changes in the stock and Forex markets. The broadening application of artificial intelligence has led to an increasing number of investors using deep learning model to predict and study stock and Forex prices. It has been proven that the fluctuation in stock and Forex price could be predicted [5]. Different from traditional statistical and econometric models, deep learning can describe complex influencing factors. Therefore, this paper investigates the different effects of different deep learning methods on stock and Forex forecasting according to existing published papers. This survey will analyze each paper from the following aspects: 1. What is the dataset of this paper? 2. What is the variable of this paper? 3. What kind of deep learning model had been adopted? 4. What is the result of the prediction model?