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 language | English (US) |
|---|---|
| Pages | 128-135 |
| Number of pages | 8 |
| Publication status | Published - Dec 1 1995 |
| Event | Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering (CIFEr) - New York, NY, USA Duration: Apr 9 1995 → Apr 11 1995 |
Other
| Other | Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering (CIFEr) |
|---|---|
| City | New York, NY, USA |
| Period | 4/9/95 → 4/11/95 |
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ASJC Scopus subject areas
- Computer Science(all)
- Economics, Econometrics and Finance(all)
- Engineering(all)
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, .