Forecasting Austrian IPOs: An application of linear and neural network error-correction models

Christian Haefke, Christian Helmenstein

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)237-251
Number of pages15
JournalJournal of Forecasting
Volume15
Issue number3
Publication statusPublished - Apr 1 1996

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Keywords

  • Cointegration analysis
  • Initial Public Offerings
  • Neural networks
  • Stock market index

ASJC Scopus subject areas

  • Modeling and Simulation
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Strategy and Management
  • Management Science and Operations Research

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