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EN
The paper contains a specific exchange rate model in the long run for the transitive economies where the key postulates of the established models such as Purchasing Power Parity, Uncovered Interest Rate Parity Condition and Monetary Approach seem to be slightly modified. The model is based on so called 'convergence gap' which is determined by number of the real factors both quantitative and qualitative. Under the conditions of the transitive economies the exchange rates are systematically deviated from the parity. The deviation is a function of the convergence gap. The exchange rate of transitive economy is therefore function of convergence gap, domestic price level and foreign price level. Since the convergence gap generally reflects the institutional structure of economy, the exchange rate is also function of the legal and institutional aspects. The theoretical consequences and the practical implications of this definition are discussed in the presented article.
EN
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.
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