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Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets
Joint Authors
Pan, Zhisong
Hu, Guyu
Tang, Siqi
Zhou, Xingyu
Zhao, Cheng
Source
Mathematical Problems in Engineering
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-04-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Stock price prediction is an important issue in the financial world, as it contributes to the development of effective strategies for stock exchange transactions.
In this paper, we propose a generic framework employing Long Short-Term Memory (LSTM) and convolutional neural network (CNN) for adversarial training to forecast high-frequency stock market.
This model takes the publicly available index provided by trading software as input to avoid complex financial theory research and difficult technical analysis, which provides the convenience for the ordinary trader of nonfinancial specialty.
Our study simulates the trading mode of the actual trader and uses the method of rolling partition training set and testing set to analyze the effect of the model update cycle on the prediction performance.
Extensive experiments show that our proposed approach can effectively improve stock price direction prediction accuracy and reduce forecast error.
American Psychological Association (APA)
Zhou, Xingyu& Pan, Zhisong& Hu, Guyu& Tang, Siqi& Zhao, Cheng. 2018. Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. Mathematical Problems in Engineering،Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1207725
Modern Language Association (MLA)
Zhou, Xingyu…[et al.]. Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. Mathematical Problems in Engineering No. 2018 (2018), pp.1-11.
https://search.emarefa.net/detail/BIM-1207725
American Medical Association (AMA)
Zhou, Xingyu& Pan, Zhisong& Hu, Guyu& Tang, Siqi& Zhao, Cheng. Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. Mathematical Problems in Engineering. 2018. Vol. 2018, no. 2018, pp.1-11.
https://search.emarefa.net/detail/BIM-1207725
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1207725