Using support vector machine with a hybrid feature selection method to the stock trend prediction
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Support vector machines and artificial neural networks are especially popular in financial market forecasting due to their ability to recognize patterns in nonlinear, dynamic time series data.
“Due to their ability to recognize patterns in nonlinear, dynamic time series data (Chang et al., 2009; Lee, 2009; Żbikowski, 2015), these methods are especially popular in financial market forecasting.”
From Bridging the divide in financial market forecasting: machine learners vs. financial economists (2016) · cites this paper as Lee, 2009
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- Authors
- Lee, M.-C.
- Affiliations
- National Pingtung University (TW)
- Corresponding author
- Lee, Ming-Chi
- ORCID
- Lee, Ming-Chi
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2009-10
- Volume
- 36
- Issue
- 8
- Pages
- 10896-10904
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W2091621250
- Cited by
- 302
- References
- 34
- Citations by year
- 2026: 11 · 2025: 12 · 2024: 17 · 2023: 29 · 2022: 32 · 2021: 24 · 2020: 29 · 2019: 26 · 2018: 15 · 2017: 12 · 2016: 15 · 2015: 14 · 2014: 20 · 2013: 15 · 2012: 11
- FWCI
- 14.00
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Expert Systems with Applications (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- stock trend prediction, support vector machine, hybrid feature selection, F-score, sequential floating forward selection, filter and wrapper methods, feature selection, back propagation neural network comparison, kernel parameter optimization, financial time series
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