Financial forecasting using support vector machines

被引：277

作者：

Cao, L ^{[1
]}

Tay, FEH ^{[1
]}

机构：

[1] Natl Univ Singapore, Dept Mech & Prod Engn, Singapore 117548, Singapore

来源：

NEURAL COMPUTING & APPLICATIONS | 2001年 / 10卷 / 02期

关键词：

back propagation algorithm; financial time series forecasting; generalisation; multi-layer perceptron; support vector machines;

D O I：

10.1007/s005210170010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The use of Support Vector Machines (SVMs) is studied in financial forecasting by comparing it with a multi-layer perceptron trained by the Back Propagation (BP) algorithm. SVMs forecast better than BP based on the criteria of Normalised Mean Square Error (NMSE). Mean Absolute Error (MAE), Directional Symmetry (DS) Correct Up (CP) trend and Correct Down (CD) trend S&P 500 daily price index is used as the data set. Since there is no structured way to choose the free parameters of SVMs, the generalisation error with respect to the free parameters of SVMs is investigated in this experiment. As illustrated in the experiment, they have little impact on the solution. Analysis of the experimental results demonstrates that it is advantageous to apply SVMs to forecast the financial rime series.

引用

页码：184 / 192

页数：9

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