C-Ascending Support Vector Machines for Financial Time Series Forecasting
Conference Proceeding Article
This paper proposes a modified version of support vector machines (SVMs), called c-ascending support vector machines (c-ASVMs), to model non-stationary financial time series. c-ASVMS are obtained by a simple modification of the regularized risk function in SVMs whereby the recent ?-insensitive errors are penalized more heavily than the distant ?-insensitive errors. This procedure is based on the prior knowledge that in the non-stationary financial time series, the recent past data could provide more important information than the distant past data. In the experiment, c-ASVMS are tested using three real futures collected from the Chicago Mercantile Market. It is shown that the c-ASVMS with the actually ordered sample data consistently forecast better than the standard SVMs, with the worst performance when the reversely ordered sample data are used. Furthermore, the c-ASVMs use fewer support vectors than those of the standard SVMs, resulting in a sparser representation of solution.
Finance and Financial Management | Portfolio and Security Analysis
2003 IEEE International Conference on Computational Intelligence for Financial Engineering: Proceedings: March 20-23, 2003, Hong Kong
City or Country
LI, Juan Cao; KOK, Seng Chua; and LIM, Kian Guan.
C-Ascending Support Vector Machines for Financial Time Series Forecasting. (2003). 2003 IEEE International Conference on Computational Intelligence for Financial Engineering: Proceedings: March 20-23, 2003, Hong Kong. 317-323. Research Collection Lee Kong Chian School Of Business.
Available at: http://ink.library.smu.edu.sg/lkcsb_research/2783