In recent years the interest of the investors in e±cient methods for the forecasting price trend of a share in financial markets has grown steadily. The aim is to accurately forecast the future behavior of the market in order to identificate the so-called "correct timing". In this paper we analyze three di®erent approaches for forecasting financial data: Autoregression, artificial neural networks and support vector machines and we will determine potentials and limits of these methods. Application to the Italian financial market is also presented.
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Titolo: | Autoregression and artificial neural networks for financial market forecast |
Autori: | |
Data di pubblicazione: | 2006 |
Rivista: | |
Abstract: | In recent years the interest of the investors in e±cient methods for the forecasting price trend of a share in financial markets has grown steadily. The aim is to accurately forecast the future behavior of the market in order to identificate the so-called "correct timing". In this paper we analyze three di®erent approaches for forecasting financial data: Autoregression, artificial neural networks and support vector machines and we will determine potentials and limits of these methods. Application to the Italian financial market is also presented. |
Handle: | http://hdl.handle.net/11581/115686 |
Appare nelle tipologie: | Articolo |