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

Author

Listed:
  • Haefke, Christian

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

  • Helmenstein, Christian

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

Abstract

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.

Suggested Citation

  • Haefke, Christian & Helmenstein, Christian, 1995. "Forecasting Austrian IPOs: An Application of Linear and Neural Network Error-Correction Models," Economics Series 18, Institute for Advanced Studies.
    • Handle: RePEc:ihs:ihsesp:18
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    File URL: https://irihs.ihs.ac.at/id/eprint/876
    File Function: First version, 1995
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    Citations

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    Cited by:

    1. Angelos Kanas, 2003. "Non-linear forecasts of stock returns," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 299-315.
    2. Olson, Dennis & Mossman, Charles, 2003. "Neural network forecasts of Canadian stock returns using accounting ratios," International Journal of Forecasting, Elsevier, vol. 19(3), pages 453-465.
    3. Geraint Johnes, 2000. "Up Around the Bend: Linear and nonlinear models of the UK economy compared," International Review of Applied Economics, Taylor & Francis Journals, vol. 14(4), pages 485-493.
    4. Helmenstein, Christian, 1995. "The Withdrawal of the State from Economic Activity: An Austrian Capital Market Perspective," Economics Series 19, Institute for Advanced Studies.
    5. Zacharias Psaradakis & Martin Sola & Fabio Spagnolo, 2004. "On Markov error-correction models, with an application to stock prices and dividends," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 19(1), pages 69-88.
    6. El Shazly, Mona R. & El Shazly, Hassan E., 1997. "Comparing the forecasting performance of neural networks and forward exchange rates," Journal of Multinational Financial Management, Elsevier, vol. 7(4), pages 345-356, December.
    7. Manzoni, Katiuscia, 2002. "Modeling credit spreads: An application to the sterling Eurobond market," International Review of Financial Analysis, Elsevier, vol. 11(2), pages 183-218.

    More about this item

    Keywords

    Initial Public Offerings; Neural Networks; Stock Market Index; Cointegration Analysis;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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