An assessment of time series methods in metal price forecasting |
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| Institution: | 1. Graduate Centre of Business, Kemmy Business School, University of Limerick, Ireland;2. Department of Economics, Kemmy Business School, University of Limerick, Ireland;1. School of Applied Sciences, Cranfield University, Cranfield, MK43 0AL, United Kingdom;2. School of Management, Cranfield University, Cranfield, MK43 0AL, United Kingdom;1. Research Base of Beijing Modern Manufacturing Development, College of Economics and Management, Beijing University of Technology, Beijing, China;2. School of Mathematical Science, Nanjing Normal University, Nanjing, Jiangsu, China;3. College of Mechanical Engineering, Chongqing University, Chongqing, China;4. Laboratory for Advanced Manufacturing Simulation and Robotics, School of Mechanical & Materials Engineering, University College Dublin, Belfield, Dublin 4, Ireland;5. Center for Polymer Studies and Department of Physics, Boston University, Boston, MA, USA;1. Department of Construction and Manufacturing Engineering, University of Oviedo, 33204 Gijón, Spain;2. Mining Exploitation and Prospecting Department, University of Oviedo, 33004 Oviedo, Spain;3. Department of Business Management, University of Oviedo, 33004 Oviedo, Spain;4. Department of Industrial Risk Assessment, Central Mining Institute, Plac Gwarków 1, 40-166 Katowice, Poland;1. Institute of Mathematics & Statistics, University of St. Gallen, Switzerland;2. School of Economics & Political Science, University of St. Gallen, Switzerland;1. School of Business, Hunan University of Science and Technology, Xiangtan 411201, China;2. School of Economics and Management, Shanghai Maritime University, Shanghai 201306, China;3. Department of Management Sciences, City University of Hong Kong, Tat Chee Avenue, Kowloon Tong, Hong Kong |
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| Abstract: | The international price for metals is pivotal in the profitability equation for mining companies. If producer prices rise, assuming production levels and costs remain the same, profits are expected to increase. Accordingly, producers welcome any means by which price instability and unpredictability can be reduced. The paper analyses the ability of two user-friendly time series forecasting techniques to predict future lead and zinc prices. The conclusion is that price forecasting is difficult. It should, however, be acknowledged that whilst neither of the two models are definitive, they are useful for the mining company vis-à-vis its planning process. In particular, the results from the analysis in this paper suggest that ARIMA modelling provides marginally better forecast results than lagged forward price modelling. The methodologies employed in this paper have a broad based application to base metal forecasting by mining companies in general, that is, the applications are transferable. |
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