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
R. De Leone, E. Marchitto, A.G. Quaranta (2006). Autoregression and artificial Neural Networks for Financial Market Forecast. NEURAL NETWORK WORLD, 16(2), 109-128.
Autoregression and artificial Neural Networks for Financial Market Forecast
QUARANTA, ANNA GRAZIA
2006
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 2006I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
