Exchange rates forecasting is an important financial problem that is receiving increasing attention especially because of its difficulty and practical applications. This paper proposes utilisation of Machine Learning methods in the field of financial praxis. Two modelling approaches - enhanced Group Method of Data Handling (GMDH) and back propagation Neural network - were employed for CZK/EUR exchange rate forecasting. Predictions were used for financial management decision simulation of a virtual company and the results indicate, that machine learning proved to be useful source of information in the area. This implies that the proposed modelling approaches can be used as a feasible solution for exchange rate forecasting in exchange rate management.
Financed by the National Centre for Research and Development under grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program:
SYNAT - “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.