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Using support vector machine with a hybrid feature selection method to the stock trend prediction
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“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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Metadata
- 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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