论文信息 - SVM application of financial time series forecasting using empirical technical indicators

SVM application of financial time series forecasting using empirical technical indicators

Support vector machines (SVMs) are promising methods of pattern recognition in 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 pattern recognition in the financial engineering domain. Compared with present machine learning methods in financial forecasting, this study does not simply work on the original time data series, but some interesting and empirical technical indicators. The experimental results show that transforming the input data space of SVM can bring good performance in finance engineering.

Jianxue Chen

[1] Kyoung-jae Kim,et al. Financial time series forecasting using support vector machines , 2003, Neurocomputing.

[2] Francis Eng Hock Tay,et al. Support vector machine with adaptive parameters in financial time series forecasting , 2003, IEEE Trans. Neural Networks.

[3] Thorsten Joachims,et al. A statistical learning learning model of text classification for support vector machines , 2001, SIGIR '01.

[4] F. Girosi,et al. Nonlinear prediction of chaotic time series using support vector machines , 1997, Neural Networks for Signal Processing VII. Proceedings of the 1997 IEEE Signal Processing Society Workshop.

[5] Thorsten Joachims,et al. A Statistical Learning Model of Text Classification for Support Vector Machines. , 2001, SIGIR 2002.

[6] Thorsten Joachims,et al. Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.