Neural network forecast combining with interaction effects

In this paper we discuss and expand recent innovations in forecast combining with artificial neural networks (ANNs). In particular, we demonstrate that ANNs can outperform traditional forecast combining procedures, such as least-squares weighting, because ANNs can account for traditionally uncaptured interaction effects between time series forecasts. Data employed in this study are price volatility forecasts for the S & P500 stock index.