Kernel principal component analysis and support vector machines for stock price prediction

被引：0

作者：

Ince, H ^{[1
]}

Trafalis, TB ^{[1
]}

机构：

[1] Fac Business Adm, Gebze Inst Technol, TR-41400 Gebze, Kocaeli, Turkey

来源：

2004 IEEE INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS, VOLS 1-4, PROCEEDINGS | 2004年

关键词：

support vector regression; Kernel Principal Component Analysis; financial time series; forecasting;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Financial time series are complex, non stationary and deterministically chaotic. Technical indicators are used with Principal Component Analysis (PCA) in order to identify the most influential inputs in the context of the forecasting model. Neural networks (NN) and support vector regression (SVR) are used with different inputs. Our assumption is that the future value of a stock price depends on the financial indicators although there is no parametric model to explain this relationship. This relationship comes from the technical analysis. Comparison shows that SVR and MLP networks require different inputs. Besides that the MLP networks outperform the SVR technique.

引用

页码：2053 / 2058

页数：6

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