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 |
| State | 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 |
Fingerprint
ASJC Scopus subject areas
- Computer Science(all)
- Economics, Econometrics and Finance(all)
- Engineering(all)
Cite this
Neural network model to exploit the econometric properties of Austrian IPOs. / Haefke, Christian; Helmenstein, Christian.
1995. 128-135 Paper presented at Proceedings of the IEEE/IAFE 1995 Computational Intelligence for Financial Engineering (CIFEr), New York, NY, USA, .Research output: Contribution to conference › Paper
}
TY - CONF
T1 - Neural network model to exploit the econometric properties of Austrian IPOs
AU - Haefke, Christian
AU - Helmenstein, Christian
PY - 1995/12/1
Y1 - 1995/12/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=0029493416&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=0029493416&partnerID=8YFLogxK
M3 - Paper
SP - 128
EP - 135
ER -