Forecasting stock market movement direction with support vector machine
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A number of studies have suggested that support vector machines outperform artificial neural networks in terms of predictive accuracy.
“A number of studies have suggested that SVM outperform ANN in terms of predictive accuracy (e.g., W.-H. Chen, Shih, & Wu, 2006; W. Huang et al., 2005; K. Kim, 2003; Ou & Wang, 2009; Tay & Cao, 2001).”
From Bridging the divide in financial market forecasting: machine learners vs. financial economists (2016) · cites this paper as W. Huang et al., 2005
Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138 · see citing works
Wu, Y., & Vapnik, V. N. (1999) · Technometrics · doi:10.2307/1271368 · Cited by 27,236 · see citing works
The Cross‐Section of Expected Stock Returns
FAMA, E. F., & FRENCH, K. R. (1992) · The Journal of Finance · doi:10.1111/j.1540-6261.1992.tb04398.x · Cited by 17,261 · see citing works
A Tutorial on Support Vector Machines for Pattern Recognition
Burges, C. J. C. (1998) · Data Mining and Knowledge Discovery · doi:10.1023/a:1009715923555 · Cited by 16,437 · see citing works
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
Cristianini, N., & Shawe-Taylor, J. (2000) · doi:10.1017/cbo9780511801389 · Cited by 13,855 · see citing works
Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212 · see citing works
Practical Methods of Optimization
Fletcher, R. (2000) · Wiley eBooks · doi:10.1002/9781118723203 · Cited by 7,649 · see citing works
The arbitrage theory of capital asset pricing
Ross, S. A. (1976) · Journal of Economic Theory · doi:10.1016/0022-0531(76)90046-6 · Cited by 7,081 · see citing works
An overview of statistical learning theory
Vapnik, V. N. (1999) · IEEE Transactions on Neural Networks · doi:10.1109/72.788640 · Cited by 6,305 · see citing works
Practical methods of optimization
(1988) · Mathematics and Computers in Simulation · doi:10.1016/0378-4754(88)90129-2 · Cited by 5,815 · see citing works
Economic Forces and the Stock Market
Chen, N., Roll, R. W., & Ross, S. A. (1986) · The Journal of Business · doi:10.1086/296344 · Cited by 5,408 · see citing works
Contrarian Investment, Extrapolation, and Risk
Lakonishok, J., SHLEIFER, A., & Vishny, R. W. (1994) · The Journal of Finance · doi:10.1111/j.1540-6261.1994.tb04772.x · Cited by 4,427 · see citing works
Klemelä, J. (2018) · Wiley series in probability and statistics · doi:10.1002/9781119409137.ch4 · Cited by 3,718 · see citing works
Practical Methods of Optimization.
Witzgall, C., & Fletcher, R. (1989) · Mathematics of Computation · doi:10.2307/2008742 · Cited by 3,614 · see citing works
Dividend yields and expected stock returns
Fama, E. F., & French, K. R. (1988) · Journal of Financial Economics · doi:10.1016/0304-405x(88)90020-7 · Cited by 3,575 · see citing works
An Introduction to Support Vector Machines and Other Kernel‐based Learning Methods
(2001) · Kybernetes · doi:10.1108/k.2001.30.1.103.6 · Cited by 3,358 · see citing works
A TUTORIAL ON SUPPORT VECTOR MACHINES FOR PATTERN RECOGNITION, DATA MINING AND KNOWLEDGE DISCOVERY
Burger, C. J. C. (1998) · Cited by 3,261 · see citing works
Permanent and Temporary Components of Stock Prices
Fama, E. F., & French, K. R. (1988) · Journal of Political Economy · doi:10.1086/261535 · Cited by 3,120 · see citing works
Amick, D. J., Cooley, W. W., & Lohnes, P. R. (1972) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2062821 · Cited by 2,941 · see citing works
Fama, E. F., & Schwert, G. W. (1977) · Journal of Financial Economics · doi:10.1016/0304-405x(77)90014-9 · Cited by 2,703 · see citing works
Stock Returns and the Term Structure
Campbell, J. Y. (1985) · Journal of Financial Economics · doi:10.1016/0304-405x(87)90045-6 · Cited by 2,402 · see citing works
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 · see citing works
Regularization Networks and Support Vector Machines
Evgeniou, T., Pontil, M., & Poggio, T. (2000) · Advances in Computational Mathematics · doi:10.1023/a:1018946025316 · Cited by 1,191 · see citing works
Forecasting stock indices: a comparison of classification and level estimation models
Leung, M. T., Daouk, H., & Chen, A. (2000) · International Journal of Forecasting · doi:10.1016/s0169-2070(99)00048-5 · Cited by 428 · see citing works
Introduction to financial forecasting
Abu‐Mostafa, Y. S., & Atiya, A. F. (1996) · Applied Intelligence · doi:10.1007/bf00126626 · Cited by 401 · see citing works
Financial Forecasting Using Support Vector Machines
Cao, L., & Tay, F. E. H. (2001) · Neural Computing and Applications · doi:10.1007/s005210170010 · Cited by 395 · see citing works
Modified support vector machines in financial time series forecasting
Tay, F. E. H., & Cao, L. (2002) · Neurocomputing · doi:10.1016/s0925-2312(01)00676-2 · Cited by 373 · see citing works
Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets
Deboeck, G. (1994) · Cited by 262 · see citing works
Measuring the Strangeness of Gold and Silver Rates of Return
Frank, M. Z., & Stengos, T. (1989) · The Review of Economic Studies · doi:10.2307/2297500 · Cited by 190 · see citing works
“Chaos” in futures markets? A nonlinear dynamical analysis
Blank, S. C. (1991) · Journal of Futures Markets · doi:10.1002/fut.3990110606 · Cited by 119 · see citing works
Tay, F. E. H., & Cao, L. J. (2001) · Intelligent Data Analysis · doi:10.3233/ida-2001-5405 · Cited by 95 · see citing works
Evidence of chaos in commodity futures prices
DeCoster, G. P., Labys, W. C., & Mitchell, D. W. (1992) · Journal of Futures Markets · doi:10.1002/fut.3990120305 · Cited by 81 · see citing works
Tay, F. E. H., & Cao, L. J. (2001) · Intelligent Data Analysis · doi:10.3233/ida-2001-5302 · Cited by 45 · see citing works
Application of support vector machines in !nancial time series forecasting
Cao, L. (2001) · Cited by 19 · see citing works
Kasabov, N. K., & Watts, M. J. (1999) · Cited by 4 · see citing works
Modi ed support vector machines in nancial time series forecasting
Tay, F. E. H., & Cao, L. J. (2002) · Cited by 4 · see citing works
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Bridging the divide in financial market forecasting: machine learners vs. financial economists
Hsu, M.-W., Lessmann, S., Sung, M.-C., Ma, T., & Johnson, J. E. V. (2016) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.05.033 · Cited by 195 · see citing works
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Booth, A., Gerding, E., & McGroarty, F. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.12.009 · Cited by 154 · see citing works
Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models
Kumar, M., & Thenmozhi, M. (2014) · International Journal of Banking, Accounting and Finance · doi:10.1504/ijbaaf.2014.064307 · Cited by 135 · see citing works
Stock Market Prediction with Gaussian Naïve Bayes Machine Learning Algorithm
Ampomah, E. K., Nyame, G., Qin, Z., Addo, P. C., Gyamfi, E. O., & Gyan, M. (2021) · Informatica · doi:10.31449/inf.v45i2.3407 · Cited by 64 · see citing works
Mabu, S., Obayashi, M., & Kuremoto, T. (2015) · Applied Soft Computing · doi:10.1016/j.asoc.2015.07.020 · Cited by 48 · see citing works
- Authors
- Huang, W., Nakamori, Y., & Wang, S.-Y.
- Affiliations
- Japan Advanced Institute of Science and Technology (JP)
- Venue
- Computers & Operations Research
- Publisher
- Elsevier BV
- Published
- 2005-10
- Volume
- 32
- Issue
- 10
- Pages
- 2513-2522
- ISSN
- 0305-0548 (ISSN-L)
- OpenAlex
- W2032170121
- Cited by
- 1,119
- References
- 36
- Citations by year
- 2026: 46 · 2025: 58 · 2024: 71 · 2023: 67 · 2022: 85 · 2021: 110 · 2020: 98 · 2019: 88 · 2018: 70 · 2017: 36 · 2016: 40 · 2015: 53 · 2014: 51 · 2013: 42 · 2012: 43
- FWCI
- 15.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers & Operations Research (publishedVersion)
- Funders
- Chinese Academy of Sciences
- Ministry of Education, Culture, Sports, Science and Technology
- National Natural Science Foundation of China
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
- Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
- Keywords
- support vector machine, Nikkei 225, financial time series classification, ensemble learning, linear discriminant analysis, quadratic discriminant analysis, BP neural network, model comparison
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