  In 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 [1] 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.
