Short-Term Financial Forecasting Using ANN Adaptive Predictors in Cascade

Our purpose is to verify the predictive performances of the artificial neural networks (ANNs) under volatile statistics and possibly incomplete information. Daily forecasts of exchange rate using exclusively primary available information for an emergent economy (such as the Romanian one) could be a proper experimental ground with such a goal. The present paper extends the previous authors' research (Dobrescu et al., 2006; Nastac et al., 2007) on the same issue to improve the accuracy of exchange rate forecasting by using a set of neural predictors in cascade, instead of a single one. The results show that the presented model, despite its proved advantages, could be further improved in order to avoid the translation into residuals of the high serial correlation present in the primary database