| Autor: |
Kollias, Stefanos, Stafylopatis, Andreas, Duch, Włodzisław, Oja, Erkki, Zimmermann, Hans Georg, Bertolini, Lorenzo, Grothmann, Ralph, Schäfer, Anton Maximilian, Tietz, Christoph |
| Zdroj: |
Artificial Neural Networks - ICANN 2006 (9783540388715); 2006, p654-663, 10p |
| Abstrakt: |
In econometrics, the behaviour of financial markets is described by quantitative variables. Mathematical and statistical methods are used to explore economic relationships and to forecast the future market development. However, econometric modeling is often limited to a single financial market. In the age of globalisation financial markets are highly interrelated and thus, single market analyses are misleading. In this paper we present a new way to model the dynamics of coherent financial markets. Our approach is based on so-called dynamical consistent neural networks (DCNN), which are able to map multiple scales and different sub-dynamics of the coherent market movement. Unlikely to standard econometric methods, small market movements are not treated as noise but as valuable market information. We apply the DCNN to forecast monthly movements of major foreign exchange (FX) rates. Based on the DCNN forecasts we develop a technical trading indicator to support investment decisions. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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