Exchange Rate Predictions in International Financial Management by Enhanced GMDH Algorithm

Exchange rate forecasting is an important financial problem that is receiving increasing attention nowadays especially because of its difficulty and host of practical applications in globalising world of today. The paper presents an enhanced MIA-GMDH-type network, discusses its design methodology and carries out some numerical experiments in the field of exchange rate forecasting. The method presented in this paper is an enhancement of self-organizing polynomial Group Method of Data Handling (GMDH) with several specific improved features - coefficient rounding and thresholding schemes and semi-randomized selection approach to pruning. The experiments carried out include exchange rate prediction and hedging case study where the predictions were used for financial management decision simulation of a virtual company. The results indicate, that the method shows promising potential of self-organizing network methodology. This implies that the proposed modelling approaches can be used as a feasible solution for exchange rate forecasting in financial management.