| Creators: | Steurer, Elmar |
|---|---|
| Title: | Quantification of sector allocation at the German stock market |
| Item Type: | Conference or Workshop Item |
| Event Title: | (Proceedings of the) Fifth International Conference Computational Finance |
| Event Location: | London, United Kingdom |
| Event Dates: | December, 15-17, 1997 |
| Projects: | ILR |
| Page Range: | pp. 333-351 |
| Additional Information: | Verlag Proceedings: Springer Science+Business Media Dordrecht |
| Date: | 1998 |
| Divisions: | Wirtschaftswissenschaften |
| Abstract (ENG): | This paper reports upon a quantitative sector allocation of the German stock market, represented by the DAX (Deutscher Aktienindex). The purpose is to present an approach how to identify an appropriate sector allocation of the 10 DAX sectors quantitatively in advance to outperform the DAX. Fundamental and technical factors are used for the explanation of each of the 10 DAX sectors. With these models forecasts for each sector are given one month ahead. Two methodologies are used for forecasting the monthly returns: linear regression and neural networks. The next step is to take in addition to these forecasts the 24-month volatilities of the considered sectors and the corresponding correlation matrix to carry out a portfolio optimisation. With these received sector weights an out-of-sample performance comparison for the out-of sample range from January 1996 until June 1997 is conducted. Thus the focus of this study lies on two points: First, whether it is possible to outperform the benchmark DAX with a quantitative method. And second, whether nonlinear methodologies deliver added value to classical econometric methods. |
| Forthcoming: | No |
| Language: | English |
| Citation: | Steurer, Elmar (1998) Quantification of sector allocation at the German stock market. In: (Proceedings of the) Fifth International Conference Computational Finance, December, 15-17, 1997, London, United Kingdom, pp. 333-351. (Advances in Computational Management Science; 2). ISBN 9780792383093 |
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