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- W2012079387 abstract "Support vector machines (SVMs) are promising methods for the prediction of financial time-series because they use a risk function consisting of the empirical error and a regularized term which is derived from the structural risk minimization principle. This study applies SVM to predicting the stock price index. In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction." @default.
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- W2012079387 date "2003-09-01" @default.
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- W2012079387 title "Financial time series forecasting using support vector machines" @default.
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- W2012079387 doi "https://doi.org/10.1016/s0925-2312(03)00372-2" @default.
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