Recognition of good prediction of gold price between MLFF and GMDH neural network

Abstract

Studies show neural networks have better results in predicting of financial time series in comparison to any linear or non-linear functional form to model the price movement. Neural networks have the advantage of simulating the non-linear models when little a priori knowledge of the structure of problem domains exist or the number of immeasurable input variables are great and system has a chaotic characteristics. Among different methods, MLFF neural network with back-propagation learning algorithm and GMDH neural network with genetic learning algorithms are used to predict gold price of the NYMEX database covering 1st January, 2002 to 13th July, 2007 period. This paper uses moving average crossover inputs and the results confirms (1) the fact that there is short-term dependence in gold price movements, (2) the EMA moving average has better result and also (3) by means of the GMDH neural networks, prediction accuracy in comparison to MLFF neural networks, can be improved. Key words: Artificial neural networks (ANN), multi layered feed forward (MLFF), group method of data handling (GMDH), simple moving average (SMA), exponential moving average (EMA), gold price.