A Survey of Forex and Stock Price Prediction Using Deep Learning
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 Bibliograph...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/4/1/9 |
| _version_ | 1797416395556782080 |
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| author | Zexin Hu Yiqi Zhao Matloob Khushi |
| author_facet | Zexin Hu Yiqi Zhao Matloob Khushi |
| author_sort | Zexin Hu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:03:38Z |
| format | Article |
| id | doaj.art-b5b35b0efa494b8f8771f6151649fd28 |
| institution | Directory Open Access Journal |
| issn | 2571-5577 |
| language | English |
| last_indexed | 2024-03-09T06:03:38Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj.art-b5b35b0efa494b8f8771f6151649fd282023-12-03T12:07:19ZengMDPI AGApplied System Innovation2571-55772021-02-0141910.3390/asi4010009A Survey of Forex and Stock Price Prediction Using Deep LearningZexin Hu0Yiqi Zhao1Matloob Khushi2School of Computer Science, The University of Sydney, Building J12/1 Cleveland St., Camperdown, NSW 2006, AustraliaSchool of Computer Science, The University of Sydney, Building J12/1 Cleveland St., Camperdown, NSW 2006, AustraliaSchool of Computer Science, The University of Sydney, Building J12/1 Cleveland St., Camperdown, NSW 2006, AustraliaPredictions 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.https://www.mdpi.com/2571-5577/4/1/9deep learningstockforeign exchangefinancial predictionsurvey |
| spellingShingle | Zexin Hu Yiqi Zhao Matloob Khushi A Survey of Forex and Stock Price Prediction Using Deep Learning Applied System Innovation deep learning stock foreign exchange financial prediction survey |
| title | A Survey of Forex and Stock Price Prediction Using Deep Learning |
| title_full | A Survey of Forex and Stock Price Prediction Using Deep Learning |
| title_fullStr | A Survey of Forex and Stock Price Prediction Using Deep Learning |
| title_full_unstemmed | A Survey of Forex and Stock Price Prediction Using Deep Learning |
| title_short | A Survey of Forex and Stock Price Prediction Using Deep Learning |
| title_sort | survey of forex and stock price prediction using deep learning |
| topic | deep learning stock foreign exchange financial prediction survey |
| url | https://www.mdpi.com/2571-5577/4/1/9 |
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