Fundamental indicators analysis toward foreign exchange (forex) prediction using neural network method-radial basis function (NN-RBF)

Abstract. Phenomenon in forex shows that trading in the forex market was generally just assumed in making decisions buy or sell, so it takes the principle of caution for trading, therefore this study aimed to provide information to traders about the methods used in making predictions buy or sell value of forex (EUR/USD), by using fundamental data that provide information in the form of news. The results of this study indicated that by using the method NN–RBF wasable to give recommendation to trader, if MSE close to zero then performance of NN–RBF program run well, for that this program can be used in predict price trend formed so that it can be used as tool and base for decision maker to do buy or sell. It was proved by the NN-RBF program performance of predictable news that shows the results of MSE news Average Hourly Earning (1,72x10-7), Consumer Confidence (35,7), CPI (6,04x10-7), GDP(9,80x10-5), Manufacturing Index (3,660), Non–Farm Employment Change (1,21), and Retail Sales (1,55x10-7). The results of this research provided a solution for traders to use the NN-RBF method in predicting the forex in orderto know the price at the forex market and prediction accuratel