Financial time series prediction using least squares support vector machines within the evidence framework

By Tony Van Gestel, Johan A. K. Suykens, Dirk-Emma Baestaens, Annemie Lambrechts, Gert R. G. Lanckriet, Bruno Vandaele, B. De Moor, Joos Vandewalle. IEEE Trans. Neural Networks, 2001

536 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/8a7d87173c8a99140ea0d0a35e1182fdfedad7dd

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Citations in the corpus

Benchmarking Least Squares Support Vector Machine Classifiers (2004, methodology): “…Sequential Minimal Optimization (SMO) (Platt, 1998) and iteratively reweighted least squares approaches (IRWLS) (Navia-Vázquez et al., 2001), while LS-SVMs (Suykens & Vandewalle, 1999b; Suykens et al., 2002; Van Gestel et al., 2001, 2002; Viaene et al., 2001) result into a set of linear equations.”
Support vector regression with chaos-based firefly algorithm for stock market price forecasting (2013, background): “…of Software, Faculty of Engineering and Information Technology, University of Technology Sydney, Ultimo, NSW, Australia c Islamic Azad University, Tafresh Branch, Young Researchers Club, Tafresh, Iran d School of Information Systems, Curtin University, Perth, WA, Australia a r t i c l e i n f o”
An Introductory Study on Time Series Modeling and Forecasting (2013, background): “g hyperplanes is given by {x∈ℜn :wT x +b =0,where w∈ℜn ,b∈ℜ}. Using SVM the problem of finding the maximum margin hyperplane reduces to solving a non-linear convex quadratic programming problem (QPP) [29, 32, 33]. SVM for Linearly Separable Data For linearly separable data the corresponding quadrati”
Support Vector Machines for Prediction of Futures Prices in Indian Stock Market (2012, methodology): “[11] have used LS-SVM for T-Bill rate and stock index pricing in US and German market.”
Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters (2005, background): “…approach has been introduced to several ﬁnancial applications such as credit rating, time series prediction, and insurance claim fraud detection (Fan & Palaniswami, 2000; Gestel et al., 2001; Huang, Chen, Hsu, Chen, & Wu, 2004; Kim, 2003; Tay & Cao, 2001; Viaene, Derrig, Baesens, & Dedene, 2002).”
Stock Trading Using RSPOP: A Novel Rough Set-Based Neuro-Fuzzy Approach (2006, background): “A number of research investigations have been published on the application of AI techniques in the technical analysis approach of forecasting stock price, but only a few presented quantitative results on trading performance using real world stock market data [19]–[24].”
Applications of Support Vector Machines for Pattern Recognition: A Survey (2002, background): “The aim of many nonlinear forecasting methods[37,39,40,41] is to predict next points of time series.”
Empirical analysis: stock market prediction via extreme learning machine (2014, methodology): “[19] use a Bayesian evidence framework and apply it to least-squares SVM regression for price and volatility predictions.”
Kernel Methods in Finance (2008, background): “Further applications in financial forecasting were presented by (Trafalis and Ince, 2000; Ince and Trafalis, 2004), (van Gestel et al., 2001), (Yang et al., 2002a), (Zhang et al., 2006) and (Kamruzzaman et al., 2003).”
A multiple-kernel support vector regression approach for stock market price forecasting (2011, unknown): “…(FNN) (Chang & Liu, 2008; Oh, Pedrycz, & Park, 2006; Zarandi, Rezaee, Turksen, & Neshat, 2009), and support vector regression (SVR) (Cao & Tay, 2001, 2003; Fernando, Julio, & Javier, 2003; Gestel et al., 2001; Pai & Lin, 2005; Tay & Cao, 2001; Valeriy & Supriya, 2006; Yang, Chan, & King, 2002).”