Cited by 303Stock Market Forecasting MethodsTime Series Analysis and ForecastingArtificial Intelligence in Healthcare

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

Lee, Ming-Chi·2009·Expert Systems with Applications

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Crossref · 10.1016/j.eswa.2009.02.038

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34 of 34 references verified0 citation errors · 0 not checkable

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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.”

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Authors
Lee, M.-C.
Affiliations
National Pingtung University (TW)
Corresponding author
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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