Modified support vector machines in financial time series forecasting

By Francis Eng Hock Tay, Lijuan Cao. Neurocomputing, 2002

388 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/e3cd07ad64750cb153de9fd17e8da8bdcd0481e0

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In Economics and Econometrics

In Electrical and Electronic Engineering

In Management Science and Operations Research

Citations in the corpus

Credit rating analysis with support vector machines and neural networks: a market comparative study (2004, methodology): “The SVM approach has been applied in several financial applications recently, mainly in the area of time series prediction and classification [42] [44].”
Trend Filtering Methods for Momentum Strategies (2011, neutral): “…matrix H as the matrix of the m concatenated lag vector of y: H = y1 y2 y3 · · · ym y2 y3 y4 · · · ym+1 y3 y4 y5 · · · ... ... ... ... . . . yt−1 yn yn+1 yn+2 · · · yt We recover the time series y by diagonal averaging: yp = 1 αp m∑ j=1 H(i,j) (10) 39See Gestel et al. (2001) and Tay and Cao 2002.”
Applications of Support Vector Machines for Pattern Recognition: A Survey (2002, methodology): “Since SVMs are known to generalize well even in high dimensional spaces under small training sample conditions [5] and have shown to be superior to traditional empirical risk minimization principle employed by most of neural networks [6], SVMs have been successfully applied to a number of applicatio”
Kernel Methods in Finance (2008, background): “With yet another contribution (Tay and Cao, 2002) enhanced SVMs to model non-stationarity of financial time series.”
Hybrid genetic algorithms and support vector machines for bankruptcy prediction (2006, background): “In ﬁnancial applications, time series prediction such as stock price indexing (Cao & Tay, 2001; Kim, 2004; Tay & Cao, 2001; Tay & Cao, 2002), and classiﬁcation such as credit rating (Huang, Chen, Hsu, Chen, & Wu, 2004), and bankruptcy (Fan & Palaniswami, 2000; Van Gestel et al., 2003) are main…”
A real-valued genetic algorithm to optimize the parameters of support vector machine for predicting bankruptcy (2007, neutral): “…approximation and regression, time series forecasting (Cao, 2003; Kim, 2003; Mukherjee, Osuna, & Girosi, 1997; Müller, Smola, Rätsch, & Schölkopf, 1999; Tay & Cao, 2001, 2002), chaotic system (Mattera & Haykin, 1999) and bankruptcy prediction (Min & Lee, 2005; Salcedo-Sanz et al., 2005).”