caa a22 4500 605470375 CHVBK 20210128100327.0 cr unu---uuuuu 210128e20150501xx s 000 0 eng 10.1007/s00500-014-1350-5 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1350-5 A critical feature extraction by kernel PCA in stock trading model [Elektronische Daten] [Pei-Chann Chang, Jheng-Long Wu] 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. Springer-Verlag Berlin Heidelberg, 2014 Kernel PCA nationallicence Feature extraction nationallicence Dimensionality reduction nationallicence Stock trading model nationallicence Financial forecasting nationallicence Chang Pei-Chann Innovation Center of Big Data & Digital Convergence and Department of Information Management, Yuan Ze University, Taoyuan, Taiwan aut Wu Jheng-Long Innovation Center of Big Data & Digital Convergence and Department of Information Management, Yuan Ze University, Taoyuan, Taiwan aut Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1393-1408 1432-7643 19:5<1393 2015 19 500 https://doi.org/10.1007/s00500-014-1350-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1350-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chang Pei-Chann Innovation Center of Big Data & Digital Convergence and Department of Information Management, Yuan Ze University, Taoyuan, Taiwan aut NATIONALLICENCE 700 1- Wu Jheng-Long Innovation Center of Big Data & Digital Convergence and Department of Information Management, Yuan Ze University, Taoyuan, Taiwan aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/5(2015-05-01), 1393-1408 1432-7643 19:5<1393 2015 19 500