Forecasting Austrian IPOs: An Application of Linear and Neural Network Error-Correction Models
Christian Haefke () and
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Christian Helmenstein: Department of Economics, Institute for Advanced Studies, Vienna
No 18, Economics Series from Institute for Advanced Studies
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 (IPOXATX). We use the significant relationship between the IPOXATX and the Austrian Stock Market Index ATX to forecast the IPOXATX. 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.
Keywords: Initial Public Offerings; Neural Networks; Stock Market Index; Cointegration Analysis (search for similar items in EconPapers)
JEL-codes: C53 C45 C43 G12 (search for similar items in EconPapers)
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