A Hybrid Gold’s Returns Prediction Model Based on Empirical Mode Decomposition

Consumers have produced extraordinary levels of demand of Gold since the beginning of the financial crisis in 2008 and investment in small coins and bars striking a record high. Since the previous decade, the prices have reached the sky, but the demand for gold remains firm. With such an enormous need for gold coming from whole over the globe, forecast gold prices are of great interest. The main aim of this study is to forecast the price of gold returns, making use of Autoregressive (AR), Empirical Mode Decomposition Autoregressive (EMDAR) and hybrid Empirical Mode Decomposition Autoregressive Neural Network (EMDARNN). The daily data consists of 4837 observations starting from Jan 1995 to June 2013, has been used in this research. After assessing the accuracy of these models by mean absolute error and mean square error, it turns out that hybrid Empirical Mode Decomposition Autoregressive Neural Network excels all the other methods and produces better forecasting with high precision. Keywords: Gold Price, Autoregressive, Empirical Mode decomposition, Artificial Neural Networ