A multiple-kernel support vector regression approach for stock market price forecasting

By Chi-Yuan Yeh, Chi-Wei Huang, Shie-Jue Lee. 2011

265 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Electrical and Electronic Engineering, Management Science and Operations Research

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Citations in the corpus

An assessment of multivariate and bivariate approaches in landslide susceptibility mapping: a case study of Duzkoy district (2015, background): “Among the various types of SVR, the most popular one is eintensive loss function that tries to find an optimum hyperplane, from which the distance to all data points is minimum (Cristianini and Shawe-Taylor 2000; Yeh et al. 2011).”
Market impact analysis via deep learned architectures (2018, methodology): “[2] use multi-kernel learning model to regress on the stock prices.”
Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information (2014, methodology): “[51] apply MKSVR to stock historical prices for making inter-day close price forecasting.”
Stock trend forecasting in turbulent market periods using neuro-fuzzy systems (2015, methodology): “There are also approaches based on support vector regression (SVr) (Yeh et al. 2011).”
Empirical analysis: stock market prediction via extreme learning machine (2014, methodology): “[2] use multi-kernel learning model to regress on the stock prices.”
Application of type-2 neuro-fuzzy modeling in stock price prediction (2012, methodology): “Then the antecedent and consequent parameters associated with the rules are refined by particle swarm optimization and least squares eywords: tock forecasting ype-2 fuzzy set SK rule elf-constructing fuzzy clustering article swarm optimization east squares estimation estimation.”
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”
A deep learning based stock trading model with 2-D CNN trend detection (2017, methodology): “While a two-stage multiple kernel learning algorithm is implemented in [10], an evolutionary algorithm is implemented in [11] to optimize hyper-parameters.”