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 language | English (US) |
|---|---|
| Pages (from-to) | 237-251 |
| Number of pages | 15 |
| Journal | Journal of Forecasting |
| Volume | 15 |
| Issue number | 3 |
| DOIs | |
| State | Published - Apr 1996 |
Keywords
- Cointegration analysis
- Initial Public Offerings
- Neural networks
- Stock market index
ASJC Scopus subject areas
- Modeling and Simulation
- Computer Science Applications
- Strategy and Management
- Statistics, Probability and Uncertainty
- Management Science and Operations Research