Neural network model to exploit the econometric properties of Austrian IPOs

Christian Haefke, Christian Helmenstein

Research output: Contribution to conferencePaper

Abstract

In this paper we apply cointegration and Granger-causality analyses to specify 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. The results suggest that trading schemes based on the forecasts significantly increase an investor's return as compared to Buy and Hold or simple Moving Average trading strategies.

Original languageEnglish (US)
Pages128-135
Number of pages8
StatePublished - 1995
EventProceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering (CIFEr) - New York, NY, USA
Duration: Apr 9 1995Apr 11 1995

Other

OtherProceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering (CIFEr)
CityNew York, NY, USA
Period4/9/954/11/95

ASJC Scopus subject areas

  • Computer Science(all)
  • Economics, Econometrics and Finance(all)
  • Engineering(all)

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  • Cite this

    Haefke, C., & Helmenstein, C. (1995). Neural network model to exploit the econometric properties of Austrian IPOs. 128-135. Paper presented at Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering (CIFEr), New York, NY, USA, .