The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Short-term stock market price trend prediction using a comprehensive deep learning system
Open accessAbstract
In the era of big data, deep learning for predicting stock market prices and trends has become even more popular than before. We collected 2 years of data from Chinese stock market and proposed a comprehensive customization of feature engineering and deep learning-based model for predicting price trend of stock markets. The proposed solution is comprehensive as it includes pre-processing of the stock market dataset, utilization of multiple feature engineering techniques, combined with a customized deep learning based system for stock market price trend prediction. We conducted comprehensive evaluations on frequently used machine learning models and conclude that our proposed solution outperforms due to the comprehensive feature engineering that we built. The system achieves overall high accuracy for stock market trend prediction. With the detailed design and evaluation of prediction term lengths, feature engineering, and data pre-processing methods, this work contributes to the stock analysis research community both in the financial and technical domains.
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Cited by
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
“Similar feature engineering-and deep learning-based approaches are presented in (Shen and Shafiq 2020; Nabipour et al. 2020; Mohapatra et al. 2022).”
BackgroundDeng, S., Zhu, Y., Yu, Y., & Huang, X. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121710 · Cited by 52
Metadata
- Authors
- Shen, J., & Shafiq, M. O.
- Venue
- Journal of Big Data
- Published
- 2020-08-28
- Volume
- 7
- Issue
- 1
- Pages
- 66-66
- ISSN
- 2196-1115 (ISSN-L)
- OpenAlex
- W3081799531
- Cited by
- 380
- References
- 39
- FWCI
- 29.80
- Type
- journal article
- Language
- en
- Open access
- gold
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