Title

C-Ascending Support Vector Machines for Financial Time Series Forecasting

Publication Type

Conference Proceeding Article

Publication Date

3-2003

Abstract

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.

Discipline

Finance and Financial Management | Portfolio and Security Analysis

Research Areas

Finance

Publication

2003 IEEE International Conference on Computational Intelligence for Financial Engineering: Proceedings: March 20-23, 2003, Hong Kong

First Page

317

Last Page

323

ISBN

9780780376540

Identifier

10.1109/CIFER.2003.1196277

Publisher

IEEE

City or Country

Hong Kong