IDEAS home Printed from https://ideas.repec.org/p/ihs/ihsesp/18.html
   My bibliography  Save this paper

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
    as

    Download full text from publisher

    File URL: http://www.ihs.ac.at/publications/eco/es-18.pdf
    File Function: First version, 1995
    Download Restriction: no

    References listed on IDEAS

    as
    1. Cowell, Frank & Schluter, Christian, 1998. "Income mobility : a robust approach," LSE Research Online Documents on Economics 2210, London School of Economics and Political Science, LSE Library.
    2. Machin, Stephen, 1996. "Wage Inequality in the UK," Oxford Review of Economic Policy, Oxford University Press, vol. 12(1), pages 47-64, Spring.
    3. Moshe Buchinsky & Jennifer Hunt, 1999. "Wage Mobility In The United States," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 351-368, August.
    4. Chamberlain, Gary, 1984. "Panel data," Handbook of Econometrics,in: Z. Griliches† & M. D. Intriligator (ed.), Handbook of Econometrics, edition 1, volume 2, chapter 22, pages 1247-1318 Elsevier.
    5. Levy, Frank & Murnane, Richard J, 1992. "U.S. Earnings Levels and Earnings Inequality: A Review of Recent Trends and Proposed Explanations," Journal of Economic Literature, American Economic Association, vol. 30(3), pages 1333-1381, September.
    6. Bo E. Honoré & Ekaterini Kyriazidou, 2000. "Panel Data Discrete Choice Models with Lagged Dependent Variables," Econometrica, Econometric Society, vol. 68(4), pages 839-874, July.
    7. Magnac, Thierry, 2000. "Subsidised Training and Youth Employment: Distinguishing Unobserved Heterogeneity from State Dependence in Labour Market Histories," Economic Journal, Royal Economic Society, vol. 110(466), pages 805-837, October.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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;

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ihs:ihsesp:18. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Doris Szoncsitz). General contact details of provider: http://edirc.repec.org/data/deihsat.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.