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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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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Wrappers for feature subset selection
Kohavi, R., & John, G. H. (1997) · Artificial Intelligence · doi:10.1016/s0004-3702(97)00043-x · Cited by 9,096
A Practical Guide to Support Vector Classication
Hsu, C., Chang, C.-C., & Lin, C. (2008) · Cited by 5,056
An Introduction to Support Vector Machines
Cristianini, N., & Shawe-Taylor, J. S. (2000) · ePrints Soton (University of Southampton) · Cited by 4,115
Feature selection for high-dimensional data: a fast correlation-based filter solution
Yu, L., & Liu, H. (2003) · Cited by 2,305
Financial time series forecasting using support vector machines
Kim, K.-J. (2003) · Neurocomputing · doi:10.1016/s0925-2312(03)00372-2 · Cited by 1,734
Correlation-based Feature Selection for Discrete and Numeric Class Machine Learning
Hall, M. A. (2000) · Cited by 1,713
Interpreting neural-network connection weights
Garson, G. D. (1991) · AI Expert archive · doi:10.5555/129449.129452 · Cited by 1,498
Application of support vector machines in financial time series forecasting
Tay, F. E. H., & Cao, L. (2001) · Omega · doi:10.1016/s0305-0483(01)00026-3 · Cited by 1,274
Efficient Analytic Approximation of American Option Values
Barone‐Adesi, G., & Whaley, R. E. (1987) · The Journal of Finance · doi:10.1111/j.1540-6261.1987.tb02569.x · Cited by 1,130
Research works printed but not linked: 2
- TEJD. (2008). Taiwan economic journal database. Available form <http://61.30.108.162/image/readme1.htm>.
- Weigend, A., (1994). On overfitting and the effective number of hidden units. In Proceedings of the 1993 connectionist models summer school (pp. 335–3420).
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Ma, Y., Liu, C., Zhang, J. T., & Liu, Y. (2022) · Applied Economics · doi:10.1080/00036846.2022.2133897 · Cited by 7
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Bolón‐Canedo, V., Donini, M., & Aiolli, F. (2015) · Padua Research Archive (University of Padova) · Cited by 7
Chen, Y.-C., Kuo, S.-M., Liu, Y., Wu, Z., & Zhang, F. (2022) · International Journal of Financial Studies · doi:10.3390/ijfs10040099 · Cited by 8
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Su, I.-F., Lin, P.-L., Chung, Y.-C., & Lee, C. (2023) · Expert Syst. J. Knowl. Eng. · doi:10.1111/exsy.13408 · Cited by 6
Cao, R., Liang, X., & Ni, Z. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35527-1_44 · Cited by 7
Tayyar, N. (2013) · Abant İzzet Baysal Üniversitesi Sosyal Bilimler Enstitüsü Dergisi · doi:10.11616/abantsbe.311 · Cited by 7
Intelligent stock data prediction using predictive data mining techniques
Kumar, P., & Bala, A. (2016) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/inventive.2016.7830218 · Cited by 7
Cai, S., Shi, Z., Jiang, C., Wang, K., Chen, L., Ai, L., & Zhang, L. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2020.611075 · Cited by 8
Li, P., & Xu, J. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/2695099 · Cited by 7
RetractedDong, Z. (2019) · PLoS ONE · doi:10.1371/journal.pone.0212487 · Cited by 7
The Extended Golden Section and Time Series Analysis
Agaian, S., & Gill, J. T., III (2017) · Frontiers in Signal Processing · doi:10.22606/fsp.2017.12003 · Cited by 7
Developing forecasting model for future pandemic applications based on COVID-19 data 2020–2022
Nawi, W. I. A. W. M., Hamid, A. A. K. A., Lola, M. S., Zakaria, S., Aruchunan, E., Gobithaasan, R. U., Zainuddin, N. H., Mustafa, W. A., Abdullah, L., Mokhtar, N. A., et al. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285407 · Cited by 6
Valença, I., Lucas, T. D. P., Ludermir, T. B., & Valença, M. (2011) · International Journal of Hybrid Intelligent Systems · doi:10.3233/his-2011-0134 · Cited by 6
Hu, Y. Q., Zhang, X., Feng, B., Xie, K., & Liu, M. (2015) · International Journal of Data Warehousing and Mining · doi:10.4018/ijdwm.2015010104 · Cited by 6
Julio, Y. F. H., Paba, M. Á. J., Leal, N., Hernández, H. M., & Nieto, W. (2017) · Iberian Conference on Information Systems and Technologies (CISTI) · doi:10.23919/cisti.2017.7975758 · Cited by 6
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Liyanage, Y. W., Zois, D., & Chelmis, C. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9053564 · Cited by 6
Lee, M., Kim, D. H., Seo, S., & Tedeschi, L. O. (2025) · Animals · doi:10.3390/ani15050687 · Cited by 5
Self-citationWang, C., Liu, G., & Zhu, L. X. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.113763 · Cited by 5
Predicting Emerging and Frontier Stock Markets Using Deep Neural Networks
Murekachiro, D., Mokoaleli‐Mokoteli, T., & Vadapalli, H. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-29516-5_68 · Cited by 5
Mokoaleli‐Mokoteli, T., Ramsumar, S., & Vadapalli, H. (2019) · Journal of Financial Management Markets and Institutions · doi:10.1142/s2282717x19500014 · Cited by 5
An Effective Stock Classification Method Via MDS Based on Modified Mutual Information Distance
Jiang, J., Shang, P., & Li, X. (2019) · Fluctuation and Noise Letters · doi:10.1142/s0219477520500182 · Cited by 5
Enhanced stock prediction using social network and statistical model
Jin, X., Guo, D., & Liu, H. (2014) · IEEE Workshop on Advanced Research and Technology in Industry Applications (WARTIA) · doi:10.1109/wartia.2014.6976495 · Cited by 5
Classification of Polarimetric SAR Images Based on Optimum SVMs Classifier Using Bees Algorithm
Samadzadegan, F., & Ferdosi, E. (2012) · Cited by 5
Stock Market Prediction using LSTM RNN ML
Kesarwani, V., & Rajesh, E. (2024) · International Conference on Advances in Computing, Communication and Networking (ICAC2N) · doi:10.1109/icac2n63387.2024.10895287 · Cited by 4
Evaluating the impact of drift detection mechanisms on stock market forecasting
Filho, L. F. P. C., & Coelho, G. P. (2023) · Knowledge and Information Systems · doi:10.1007/s10115-023-02025-y · Cited by 4
Ensemble Computations on Stock Market: A Standardized Review for Future Directions
Gandhmal, D. P., & Kumar, K. (2019) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct.2019.8869158 · Cited by 4
Stock Trading Based on Principal Component Analysis and Clustering Analysis
Guo, Y. (2020) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/740/1/012129 · Cited by 4
Yang, J., Kim, J., Farshadmanesh, P., Sakurahara, T., Reihani, S., Blake, C., & Mohaghegh, Z. (2022) · Progress in Nuclear Energy · doi:10.1016/j.pnucene.2022.104411 · Cited by 4
An Evaluation of Stock Market Prediction using Supervised Machine Learning Techniques
Sagar, P. L., Maheshbabu, C., Teja, G. V. S., Vishnu, C., Sai, D. V. S., & Mahesh, C. (2023) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict57646.2023.10133970 · Cited by 4
Mahmoodi, A., Hashemi, L., Mahmoodi, A., Mahmoodi, B., & Jasemi, M. (2023) · Journal of Capital Markets Studies · doi:10.1108/jcms-04-2023-0013 · Cited by 4
A Financial Deep Learning Framework
Li, Z., Liao, Y., Hu, B., Ni, L., & Lu, Y. (2022) · International Journal of Web Services Research · doi:10.4018/ijwsr.302640 · Cited by 5
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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