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Forecasting Austrian IPOs: An application of linear and neural network error‐correction models
Author(s) -
Haefke Christian,
Helmenstein Christian
Publication year - 1996
Publication title -
journal of forecasting
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.543
H-Index - 59
eISSN - 1099-131X
pISSN - 0277-6693
DOI - 10.1002/(sici)1099-131x(199604)15:3<237::aid-for621>3.0.co;2-5
Subject(s) - estimator , artificial neural network , cointegration , stock market index , econometrics , index (typography) , feedforward neural network , computer science , granger causality , stock market , feed forward , construct (python library) , variance (accounting) , economics , artificial intelligence , mathematics , statistics , engineering , paleontology , accounting , horse , world wide web , biology , programming language , control engineering
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 ATX ). We use the significant relationship between the IPOX ATX and the Austrian Stock Market Index ATX to forecast the IPOX ATX . 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.