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| Titel: |
Kernel principal component analysis and support vector machines for stock price prediction |
| Auteur: |
Ince, Huseyin Trafalis, Theodore B. |
| Verschenen in: |
IIE transactions |
| Paginering: | Jaargang 39 (2007) nr. 6 pagina's 629-637 |
| Jaar: |
2007-06 |
| Inhoud: |
Technical indicators are used with two heuristic models, kernel principal component analysis and factor analysis in order to identify the most influential inputs for a forecasting model. Multilayer perceptron (MLP) networks and support vector regression (SVR) are used with different inputs. We assume that the future value of a stock price/return depends on the financial indicators although there is no parametric model to explain this relationship, which comes from the technical analysis. Comparison studies show that SVR and MLP networks require different inputs. Furthermore, proposed heuristic models produce better results than the studied data mining methods. In addition to this, we can say that there is no difference between MLP networks and SVR techniques when we compare their mean square error values. |
| Uitgever: |
Taylor & Francis |
| Bronbestand: |
Elektronische Wetenschappelijke Tijdschriften |
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