The Prediction of Crude Oil Futures Prices - Comparison aming Backpropagation Neural Networks,Elman Recurrent Neural Networks and Recurrent Fuzzy Neural Networks.

• Main Authors: Jui-Wen Lin, 林瑞文
• Other Authors: Wei-Shan Hu
• Format: Others
• Language: zh-TW
• Published: 2005
• Online Access: http://ndltd.ncl.edu.tw/handle/17140925737594266130

碩士 === 中原大學 === 企業管理研究所 === 94 === During the past three years, oil price has changed dramatically and terrorists’ attacks caused the turbulent uneasiness of the global economy. Consequently, governments and corporate managers around the world actively sought effective methods to forecast the oil price more accurately than before for the purposes of hedging and arbitraging. The purpose of this study is to predict the crude oil futures prices more accurately than traditional methods by using three popular non-parametric methods, namely, Backpropagation Neutral Networks (BPNs), Elman Recurrent Neural Networks (ERNNs), and Recurrent Fuzzy Neral Networks (RFNNs). This work also compares the learning and predictive performance among BPNs, ERNNs and RFNNs, and explores how training time impacts predictive accuracy. The results show that the use of these three non-parametric methods to forecast the crude oil futures prices was appropriate since their values of MSE were all less than 0.0026767. Additionally, the learning ability was consistent by employing different training times. This investigation also indicates that the more training times the networks took, the better learning performance the networks have under most circumstances, the only exceptional case occurs at part two under FRNN model, where MSE is slightly less than that obtained from part three. Regarding the predictive power of the three artificial neural networks (ANNs), this study finds that RFNNs has the best predictive power and BPN has the least predictive power among the three ANNs. This investigation also confirms that the predictive power can be enhanced by combining Fuzzy theory with the Recurrent Neural Network.