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Forecasting Austrian IPOs: An application of linear and neural network error-correction models

✍ Scribed by Christian Haefke; Christian Helmenstein

Publisher
John Wiley and Sons
Year
1996
Tongue
English
Weight
942 KB
Volume
15
Category
Article
ISSN
0277-6693

No coin nor oath required. For personal study only.

✦ Synopsis

In this paper we apply cointegration and Granger-causality analyses to construct linear and neural network error-correction models for an Austrian Initial Public Offerings Index (IPOX,,). We use the significant relationship between the IPOX,, and the Austrian Stock Market Index ATX to forecast the IPOX,,. For prediction purposes we apply augmented feedforward neural networks whose architecture is determined by Sequential Network Construction with the Schwartz Information Criterion as an estimator for the prediction risk. Trading based on the forecasts yields results superior to Buy and Hold or Moving Average trading strategies in terms of mean-variance considerations.

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