Foreign exchange rate forecasting with a virtual-expert partial-consensus fuzzy-neural approach for semiconductor manufacturers in Taiwan

Abstract

Accurately forecasting the foreign exchange rate is very important to export-oriented enterprises, like semiconductor manufacturers in Taiwan. To this end, a virtual-expert partial-consensus fuzzy-neural approach is proposed in this study. In the proposed methodology, instead of calling a number of experts in the field, a committee of virtual experts is formed, and then they are asked to provide views on fuzzy forecasts. For each virtual expert, the corresponding fuzzy linear regression (FLR) equation is constructed to predict the foreign exchange rate. Each FLR equation can be fitted by solving two equivalent non-linear programming problems, based on the virtual experts’ views. To aggregate these fuzzy foreign exchange rate forecasts, a two-step aggregation mechanism is applied. To evaluate the effectiveness of the proposed methodology, the real case of forecasting the foreign exchange rate of NTD for USD is used.