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Forecasting Austrian IPOs: An Application of Linear and Neural Network Error-Correction Models

Listed author(s):
  • Haefke, Christian

    (Department of Economics, Institute for Advanced Studies, Vienna)

  • Helmenstein, Christian

    (Department of Economics, Institute for Advanced Studies, Vienna)

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.

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File URL: http://www.ihs.ac.at/publications/eco/es-18.pdf
File Function: First version, 1995
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Paper provided by Institute for Advanced Studies in its series Economics Series with number 18.

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Length: 21 pages
Date of creation: Dec 1995
Handle: RePEc:ihs:ihsesp:18
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