The string prediction models as an invariants of time series in forex market
AbstractIn this paper we apply a new approach of the string theory to the real financial market. It is direct extension and application of the work  into prediction of prices. The models are constructed with an idea of prediction models based on the string invariants (PMBSI). The performance of PMBSI is compared to support vector machines (SVM) and artificial neural networks (ANN) on an artificial and a financial time series. Brief overview of the results and analysis is given. The first model is based on the correlation function as invariant and the second one is an application based on the deviations from the closed string/pattern form (PMBCS). We found the difference between these two approaches. The first model cannot predict the behavior of the forex market with good efficiency in comparison with the second one which is, in addition, able to make relevant profit per year.
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Bibliographic InfoPaper provided by arXiv.org in its series Papers with number 1109.0435.
Date of creation: Sep 2011
Date of revision: Oct 2012
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Web page: http://arxiv.org/
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-09-16 (All new papers)
- NEP-FMK-2011-09-16 (Financial Markets)
- NEP-FOR-2011-09-16 (Forecasting)
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