Using Qualitative Information and Neural Networks for Forecasting Purposes in Financial Time Series

In the Italian financial market, stock fluctuations are highly dependent on political and economic events. For this reason, any realistic forecast should consider this kind of information. In this paper we show a way to include economic and political events in order to forecast a financial time series. Then we applied neural networks, econometric analysis and some recent non-parametric regression models to empirical data observed over a period of 61 weeks. The respective performances of the different approaches were then compared.