The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Deep learning-based feature engineering for stock price movement prediction
Abstract
Abstract Stock price modeling and prediction have been challenging objectives for researchers and speculators because of noisy and non-stationary characteristics of samples. With the growth in deep learning, the task of feature learning can be performed more effectively by purposely designed network. In this paper, we propose a novel end-to-end model named multi-filters neural network (MFNN) specifically for feature extraction on financial time series samples and price movement prediction task. Both convolutional and recurrent neurons are integrated to build the multi-filters structure, so that the information from different feature spaces and market views can be obtained. We apply our MFNN for extreme market prediction and signal-based trading simulation tasks on Chinese stock market index CSI 300. Experimental results show that our network outperforms traditional machine learning models, statistical models, and single-structure(convolutional, recurrent, and LSTM) networks in terms of the accuracy, profitability, and stability.
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Short-term stock market price trend prediction using a comprehensive deep learning system
Shen, J., & Shafiq, M. O. (2020) · Journal of Big Data · doi:10.1186/s40537-020-00333-6 · Cited by 380
Artificial Intelligence Applied to Stock Market Trading: A Review
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Stock-Price Forecasting Based on XGBoost and LSTM
Hoang Vuong, P., Tan Dat, T., Khoi Mai, T., Hoang Uyen, P., & The Bao, P. (2022) · Computer Systems Science and Engineering · doi:10.32604/csse.2022.017685 · Cited by 100
Hao, Y., & Gao, Q. (2020) · Applied Sciences · doi:10.3390/app10113961 · Cited by 95
Reinforcement Learning in Stock Trading
Dang, Q.-V. (2020) · Advances in Intelligent Systems and Computing · doi:10.1007/978-3-030-38364-0_28 · Cited by 57
Metadata
- Authors
- Long, W., Lu, Z., & Cui, L.
- Venue
- Knowledge-Based Systems
- Publisher
- Elsevier BV
- Published
- 2019-01
- Volume
- 164
- Pages
- 163-173
- ISSN
- 0950-7051 (ISSN-L)
- OpenAlex
- W2900880305
- Cited by
- 483
- References
- 12
- FWCI
- 33.58
- Type
- journal article
- Language
- en
- Open access
- closed
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