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EnglishIn recent years the interest of the investors in efficient 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 different 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. ©ICS AS CR 2006
| Titolo: | Autoregression and artificial Neural Networks for Financial Market Forecast |
| Autore/i: | R. De Leone; E. Marchitto; QUARANTA, ANNA GRAZIA |
| Autore/i Unibo: | |
| Anno: | 2006 |
| Rivista: | |
| Abstract: | In recent years the interest of the investors in efficient 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 different 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. ©ICS AS CR 2006 |
| Data prodotto definitivo in UGOV: | 2009-12-19 16:37:40 |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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http://hdl.handle.net/11585/81454
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