Name
Abstract | ||
|---|---|---|
This paper presents a kernel-based principal component analysis (kernel PCA) to extract critical features for improving the performance of a stock trading model. The feature extraction method is one of the techniques to solve dimensionality reduction problems (DRP). The kernel PCA is a feature extraction approach which has been applied to data transformation from known variables to capture critical information. The kernel PCA is a kernel-based data mapping tool that has characteristics of both principal component analysis and non-linear mapping. The feature selection method is another DRP technique that selects only a small set of features from known variables, but these features still indicate possible collinearity problems that fail to reflect clear information. However, most feature extraction methods use a variable mapping application to eliminate noisy and collinear variables. In this research, we use the kernel-PCA method in a stock trading model to transform stock technical indices (TI) which allows features of smaller dimension to be formed. The kernel-PCA method has been applied to various stocks and sliding window testing methods using both half-year and 1-year testing strategies. The experimental results show that the proposed method generates more profits than other DRP methods on the America stock market. This stock trading model is very practical for real-world application, and it can be implemented in a real-time environment. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/s00500-014-1350-5 | Soft Computing - A Fusion of Foundations, Methodologies and Applications |
Keywords | Field | DocType |
Kernel PCA, Feature extraction, Dimensionality reduction, Stock trading model, Financial forecasting | Data mining,Dimensionality reduction,Feature selection,Computer science,Kernel principal component analysis,Artificial intelligence,Stock market,Kernel (linear algebra),Collinearity,Pattern recognition,Feature extraction,Machine learning,Principal component analysis | Journal |
Volume | Issue | ISSN |
19 | 5 | 1433-7479 |
Citations | PageRank | References |
4 | 0.42 | 22 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pei-Chann Chang | 1 | 1752 | 109.32 |
| Jheng-Long Wu | 2 | 95 | 9.54 |