The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets
Open accessAbstract
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.
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Cited by
Bandi, A., Adapa, P. V. S. R., & Kuchi, Y. E. V. P. K. (2023) · Future Internet · doi:10.3390/fi15080260 · Cited by 638
A systematic review of fundamental and technical analysis of stock market predictions
Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-019-09754-z · Cited by 547
Sonkavde, G., Dharrao, D. S., Bongale, A. M., Deokate, S. T., Doreswamy, D., & Bhat, S. K. (2023) · International Journal of Financial Studies · doi:10.3390/ijfs11030094 · Cited by 262
“Another proposed method uses Ensemble LSTM with CNN on stock indexes for training adversarial networks, which can help forecast high-frequency stock and has advantages like adversarial training and reducing direction prediction loss and forecast error loss (Zhou et al. 2018).”
MethodStock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review
Li, A. W., & Bastos, G. S. (2020) · IEEE Access · doi:10.1109/access.2020.3030226 · Cited by 186
Generative Adversarial Networks for Spatio-temporal Data: A Survey
Gao, N., Xue, H., Shao, W., Zhao, S., Qin, K. K., Prabowo, A., Rahaman, M. S., & Salim, F. D. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3474838 · Cited by 127
Metadata
- Authors
- Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C.
- Venue
- Mathematical Problems in Engineering
- Publisher
- Wiley
- Published
- 2018
- Volume
- 2018
- Pages
- 1-11
- ISSN
- 1024-123X (ISSN-L)
- 1563-5147
- OpenAlex
- W2796929742
- Cited by
- 258
- References
- 25
- FWCI
- 23.79
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Zhou, X., Pan, Z., Hu, G., Tang, S., & Zhao, C. (2018). Stock Market Prediction on High-Frequency Data Using Generative Adversarial Nets. Mathematical Problems in Engineering. https://doi.org/10.1155/2018/4907423
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