Title: The forecast for precious metal indexes and precious metal ETFs: an artificial neural network analysis

Authors: Do Thi Van Trang

Addresses: Department of Finance, Banking Academy, Hanoi, Vietnam

Abstract: This paper applies chaos effect, grey relational analysis (GRA) and artificial neural network (ANN) to forecast the return volatility of precious metals and precious metal ETFs. The results showed that all the data series performed deterministic chaos. The GRA results emphasised in all samples, the West Texas Intermediate (WTI) index represents the greatest influence, followed by stock index, exchange rate, commodity research bureau (CRB) index, volatility index, interest rate and put-call (P/C) ratio. Moreover, the backpropagation network (BPN) model is the most powerful model among four ANN models like BPN, radial basic function (RBP), recurrent neural network (RNN) and time-delay recurrent neural network (TDRNN) in forecasting precious metals and precious metal ETFs. 'All variables' group has stronger influence than high- or low-grey relational grade (GRG) variables for predicting precious metals. Whereas, precious metal ETFs have better forecasting results by using 'all variables' group for iShares Silver Trust (SLV) and ETFs physical platinum shares (PPLT) and high-GRG variables for SPDR gold shares (GLD) and ETFs physical palladium shares (PALL). Therefore, investors and traders can get profit through linkage seven determinants in forecasting precious metals and precious metal ETFs.

Keywords: precious metals; precious metal ETFs; exchange traded funds; chaos effect; grey relational analysis; GRA; artificial neural networks; ANNs; forecasting; returns volatility; forecasting; stock index; exchange rate; CRB index; volatility index; interest rates; put-call ratio; silver; platinum; gold.

DOI: 10.1504/IJBD.2015.071403

International Journal of Bonds and Derivatives, 2015 Vol.1 No.3, pp.237 - 272

Received: 17 Dec 2014
Accepted: 29 Jan 2015
Published online: 25 Aug 2015 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article