The Study on the Prediction Model of the Euro Exchange Rate by Using Gray Relational Analysis and Neural Network

• Main Authors: Wei-Cian Lin, 林維謙
• Other Authors: Liang-Chien Lee
• Format: Others
• Language: zh-TW
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/39637492086539627704

碩士 === 義守大學 === 財務金融學系 === 104 === Euro was launched in 1999, it faces currency issues beyond US dollar and its introduction represents important influences on international monetary and financial system. Hong (2006) pointed out that euro has become international major currency and get an important international status in international trade, financial and monetary markets. European Union member countries are numerous and constitution complicacy, coupled with political aspects and debt crisis affected the euro exchange rate volatility. Hence, case study of the prediction for euro exchange rate and its volatility are meaningful and valuable. This paper investigate the prediction of euro exchange rate changes model using back-propagation neural network to reduce investment risk and improve profitability. The samples used in this paper are euro exchange rate changes data for the period between 2004 and 2013. First, we find out the impact factors and correlation of euro exchange rate using grey relational analysis. Then, the high correlation impact factors are derived into back-propagation neural network to construct the prediction of euro exchange rate changes model. We also calculate the prediction of euro exchange rate changes model using multiple regression method. Finally, the optimization model for investor decision making has chosen based on comparison between multiple regression method and back-propagation neural network model. The empirical results show that high correlation impact factors are filtered by grey relational analysis and derived into multiple regression model can improve predication performance, it reveals that grey relational analysis is a good screening tool. The MSE value of back-propagation neural network and the error of actual sample verification are lower than multiple regression model, but the accuracy rate of prediction changes are consistent which is up to 83.33%. As a conclusion, neural network model has preferable predication performance.