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

By Xingyu Zhou, Zhisong Pan, Guyu Hu, Siqi Tang, Cheng Zhao. 2018

242 citations recorded by the source corpus.

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.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research, Generative Models, Time-Series & Forecasting

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

1. Henry Leung
2. Weiwei Jiang
3. Jun Zhao
4. King Fai Ma
5. Zhongyang Han
6. Amir Mosavi
7. Wei Wang

In Finance

1. Weiwei Jiang

In Management Science and Operations Research

1. Henry Leung
2. Pooyan Nayyeri
3. Hamed Jabani
4. Weiwei Jiang
5. Bourahla Omar
6. Abdul Jabbar
7. Xi Li
8. Jun Zhao

In Generative Models

1. Weiwei Jiang
2. Bourahla Omar
3. Abdul Jabbar
4. Xi Li

In Time-Series & Forecasting

1. Pooyan Nayyeri
2. Hamed Jabani
3. Weiwei Jiang
4. Amir Mosavi
5. Mojtaba Nabipour

Citations in the corpus

• A Review of Deep Learning Models for Time Series Prediction (2019, background): “For instance, [188] presented a study of stock market prediction on high-frequency using GAN.”
• Deep Learning for Stock Market Prediction (2020, methodology): “[35] applied LSTM and CNN on high-frequency data from the stock market with the approach of rolling partition training and testing set to evaluate the update cycle effect on the performance of models.”
• Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…& Vateekul (2018); Pang et al. (2018); Sezer & Ozbayoglu (2018); Tang & Chen (2018); Tran et al. (2018); Wu et al. (2018); Yang et al. (2018); Zhou et al. (2018); de A. Arajo et al. (2019); Chen & Ge (2019); Deng et al. (2019); Feng et al. (2019a); Guang et al. (2019); Karathanasopoulos &…”
• A Survey on Generative Adversarial Networks: Variants, Applications, and Training (2020, background): “…social robot [189], cipher cracking [190], auxiliary automatic driving [236], continual learning [237], molecule development in oncology [238], GANs for finance [239-242], GANs for textile [243, 244], GANs for e-commerce [245], GANs for fluid-flow [246, 247 ], and in many others dominant areas.”