Financial Forecasting Using Support Vector Machines

By Lijuan Cao, Francis Eng Hock Tay. Neural computing & applications (Print), 2001

425 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/e84fed9a98d2495bb5f21a78d9f414ff32041e16

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In Artificial Intelligence

In Management Science and Operations Research

In Signal Processing

Citations in the corpus

A multiple-kernel support vector regression approach for stock market price forecasting (2011, neutral): “…neural networks (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, &…”
Support vector machines experts for time series forecasting (2003, background): “over-3tting problem, eventually achieving high generalization performance in solving various time series forecasting problems [2,23–26].”
Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information (2014, methodology): “[4,5] also find that SVM has the better performance based on the metrics of normalized mean square error and mean absolute error.”
Financial time series prediction using polynomial pipelined neural networks (2008, background): “In multivariate approaches, any indication whether or not it is directly related to the output can be incorporated as the input variable (Cao & Tay, 2001).”
Optimal partition algorithm of the RBF neural network and its application to financial time series forecasting (2005, background): “The S&P 500 data cover the time period from 01/04/1993 up to 12/31/1995, which possess obviously unsymmetrical characteristics [3], so preprocessing on the data needs to be performed.”
Financial time series forecasting using independent component analysis and support vector regression (2009, methodology): “It has been successfully applied in different problems of time series prediction such as production value forecast of machinery industry, engine reliability prediction, wind speed prediction and financial time series forecasting [7, 9, 26, 30, 45, 48, 55, 56, 57].”
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…”
Forecasting stock market movement direction with support vector machine (2005, positive): “Some applications of SVM to ÿnancial forecasting problems have been reported recently [9] [10] [11] [12] [13].”
Support vector machine with adaptive parameters in financial time series forecasting (2003, background): “A more detailed description on this work can also be found in our earlier papers [21], [22].”
A Hybrid Machine Learning System for Stock Market Forecasting (2008, methodology): “SVMs were first used by Tay & Cao for financial time series forecasting [13], [14], [15].”
Pattern graph tracking-based stock price prediction using big data (2018, methodology): “First, regarding data mining studies using daily stock data, there are prediction research works based on support vector machine (SVM) ([8, 24]) in order to determine whether the new pattern data belongs to a certain pattern category, artiﬁcial neural network (ANN) ([31, 32]), to have good…”