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Title: | A survey of forex and stock price prediction using deep learning |
Authors: | Hu, Z Zhao, Y Khushi, M |
Keywords: | deep learning;stock;foreign exchange;financial prediction;survey |
Issue Date: | 2-Feb-2021 |
Publisher: | MDPI |
Citation: | Hu, Z., Zhao, Y. and Khushi, M. (2021) 'A survey of forex and stock price prediction using deep learning', Applied System Innovation, 2021, 4 (1), 9,. pp. 1 - 30. doi: 10.3390/ASI4010009. |
Abstract: | Copyright © 2021 by the authors. 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. |
Description: | Data Availability Statement: Not applicable. |
URI: | https://bura.brunel.ac.uk/handle/2438/27044 |
DOI: | https://doi.org/10.3390/ASI4010009 |
Other Identifiers: | ORCID iD: Matloob Khushi https://orcid.org/0000-0001-7792-2327 9 |
Appears in Collections: | Dept of Computer Science Research Papers |
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