Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets

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 adversar...

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Bibliographic Details
Published inMathematical problems in engineering Vol. 2018; no. 2018; pp. 1 - 11
Main Authors Tang, Siqi, Hu, Guyu, Pan, Zhisong, Zhou, Xingyu, Zhao, Cheng
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2018
Hindawi
Hindawi Limited
Subjects
Artificial neural networks
Computer simulation
Economic forecasting
Electronic trading systems
High frequency trading
Markets
Mathematical problems
Neural networks
Stock exchanges
Training
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Summary: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.
ISSN:1024-123X
1563-5147
DOI:10.1155/2018/4907423