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Neural network forecast combining with interaction effects

✍ Scribed by R. Glen Donaldson; Mark Kamstra

Publisher: Elsevier Science
Year: 1999
Tongue: English
Weight: 171 KB
Volume: 336
Category: Article
ISSN: 0016-0032
DOI: 10.1016/s0016-0032(98)00018-0

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✦ Synopsis

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 & P 500 stock index.

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