Within the last few decades, copper has been identified as one of the most applicable metals by many researchers. These researchers have also been enthusiastic to predict the price of this valuable metal. These days, the available technical analysis methods have been highly applied in the financial markets. Moreover, the researchers have used these methods to predict the suitable price tr...

Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent years, the classic estimation methods cannot correctly estimate the volatility. In order to...

abstract nowadays, due to the environmental uncertainty and rapid development of new technologies, economic variables are often predicted by using less data and short-term timeframes. therefore, prediction methods which require fewer amounts of data are needed. auto regressive integrated moving average (arima) model and artificial neural networks (anns) need large amounts of data to achieve acc...

Metal price is one of the most important parameters in the calculation of cut- off grade. The cut- off grade has the main role in determination of mine layout. Mine layout actuates mineable reserve, mine life and economic profitability. Not considering the uncertainty in metal prices can lead to a non-optimal layout. In this paper optimum underground mine layout is determined by expected utilit...

modelling and forecasting stock market is a challenging task for economists and engineers since it has a dynamic structure and nonlinear characteristic. this nonlinearity affects the efficiency of the price characteristics. using an artificial neural network (ann) is a proper way to model this nonlinearity and it has been used successfully in one-step-ahead and multi-step-ahead prediction of di...

this paper employs a general non-linear analysis tool to analyse the nature of time series associated with the price (returns) of a particular company in tehran stock exchange. it is shown that the behavior of the process associated with the price (returns) time-series of this company is weakly chaotic, and due to the non-random behavior of the process, short term prediction of stock price is p...

using nonlinear mathematical analysis, on the data obtained for.time seriesof shahd-iran price during 3.5 years, the characteristics,of the process associated with this, is analysed. analysing the behaviour of the time series associated with returns is indicative ofits short-term predictability nature. however, employing analysis regarding the correlation dimension estimate. it is indicated tha...

‎financial market modeling and prediction is a difficult problem and drastic changes of the price causes nonlinear dynamic that makes the price prediction one of the most challenging tasks for economists‎. ‎since markets always have been interesting for traders‎, ‎many traders with various beliefs are highly active in a market‎. ‎the competition among two agents of traders‎, ‎namely trend follo...

This paper employs a general non-linear analysis tool to analyse the nature of time series associated with the price (returns) of a particular company in Tehran Stock Exchange. It is shown that the behavior of the process associated with the price (returns) time-series of this company is weakly chaotic, and due to the non-random behavior of the process, short term prediction of stock price is p...