Ore Price Research Articles

Artificial intelligence techniques recruitment for gold ore (Crude gold) price prediction

Artificial intelligence techniques recruitment for gold ore (Crude gold) price prediction

Iron Ore Price Forecast based on a Multi-Echelon Tandem Learning Model

Iron Ore Price Forecast based on a Multi-Echelon Tandem Learning Model

Navigating Market Sentiments: A Novel Approach to Iron Ore Price Forecasting with Weighted Fuzzy Time Series

The global iron ore price is influenced by numerous factors, thus showcasing a complex interplay among them. The collective expectations of market participants over time shape the variations and trends within the iron ore price time series. Consequently, devising a robust forecasting model for the volatility of iron ore prices, as well as for other assets connected to this commodity, is critical for guiding future investments and decision-making processes in mining companies. Within this framework, the integration of artificial intelligence techniques, encompassing both technical and fundamental analyses, is aimed at developing a comprehensive, autonomous hybrid system for decision support, which is specialized in iron ore asset management. This approach not only enhances the accuracy of predictions but also supports strategic planning in the mining sector.

Improving multilayer perceptron neural network using two enhanced moth-flame optimizers to forecast iron ore prices

Abstract The quality of the output produced by the multi-layer perceptron neural network depends on the careful selection of its weights and biases. The gradient descent technique is commonly used for choosing MLP’s optimal configuration, but it can suffer from being stuck in local optima and slow convergence toward promising regions in the search space. In this article, we propose two new optimization algorithms based on the moth-flame optimization algorithm (MFO), which mimics moths’ special navigation methods at night. We use these algorithms to enhance the performance of the training process of the MLP neural network. To demonstrate the effectiveness of our approach, we apply it to the problem of predicting iron ore prices, which plays an important role in the continuous development of the steel industry. We use a large number of features to predict the iron ore price, and we select a promising set of features using two feature reduction methods: Pearson’s correlation and a newly proposed categorized correlation. Surprisingly, new features not mentioned in the literature are discovered, and some are discarded. The time series dataset used has been extracted from several sources and pre-processed to fit the proposed model. We compare our two proposed MFO algorithms, the roulette wheel moth-flame optimization algorithm and the global best moth-flame optimization algorithm, against four swarm intelligence algorithms and five classical machine learning techniques when predicting the iron ore price. The results acquired indicate the superior performance of the suggested algorithms concerning prediction accuracy, root-mean-square error, mean-square error, average absolute relative deviation, and mean absolute error. Overall, our work presents a promising approach for improving the performance of MLP neural networks, and it demonstrates its effectiveness in the challenging problem of predicting iron ore prices.

Iron Ore Price Prediction Based on Multiple Linear Regression Model

The fluctuation of iron ore prices is one of the most important factors affecting policy. Therefore, the accurate prediction of iron ore prices has significant value in analysis and judgment regarding future changes in policies. In this study, we propose a correlation analysis to extract eight influencing factors of iron ore prices and introduce multiple linear regression analysis to the prediction. With historical data, we establish a model to forecast iron ore prices from 2020 to 2024. Taking prices in 2018 and 2019 as samples to test the applicability of the model, we obtain an acceptable level of error between the predicted iron ore prices and the actual prices. The prediction model based on multiple linear regression has high prediction accuracy. Iron ore prices will show a relatively stable upward trend over the next five years without the effects of COVID-19.

Exploring the Factors Influencing the Impact of the COVID-19 Pandemic on Global Shipping: A Case Study of the Baltic Dry Index

The outbreak of COVID-19 in 2020 resulted in notable disruptions to global shipping and the global economy. As a key indicator influenced by supply and demand conditions in the shipping industry, the Baltic Dry Index (BDI) serves as an early economic indicator for global economic production. Contrary to expectations of decline, the BDI has exhibited a substantial increase. This research paper aims to investigate the impact of the COVID-19 pandemic on global shipping through a comprehensive analysis of the BDI. The study incorporates data spanning from 2019 to 2021, encompassing the pre- and post-pandemic periods. It examines 13 independent variables, including raw material prices (such as iron ore prices), international scrap steel prices, energy prices, stock market indexes, international commodity price volatility (as represented by the Commodity Research Bureau Index), global port calls, and confirmed COVID-19 cases. The primary objective is to explore the factors influencing the BDI and how they were affected by the pandemic. The study employs stepwise regression to select variables and build models before and after the pandemic. The findings of this study elucidate the prominent factors that influence the BDI in different temporal contexts. Before the outbreak, the BDI was notably impacted by variables, including the US Dollar Index (positive relationship), Brent, Port Calls, and CRB Index. However, a discernible shift in the relative significance of these factors has been observed in the post-pandemic period. Specifically, the US Dollar Index now exhibits a negative relationship with the BDI, whereas variables such as Port Calls, Iron Price, Steel Scrap Price, and confirmed COVID-19 cases had attained heightened prominence in shaping the dynamics of the freight index. These findings underscored the dynamic nature of the factors influencing the BDI, particularly in light of the unique circumstances brought about by the COVID-19 pandemic.

Green steel: Synergies between the Australian iron ore industry and the production of green hydrogen

Green steel, produced using renewable energy and hydrogen, presents a promising avenue to decarbonize steel manufacturing and expand the hydrogen industry. Australia, endowed with abundant renewable resources and iron ore deposits, is ideally placed to support this global effort. This paper's two-step analytical approach offers the first comprehensive assessment of Australia's potential to develop green steel as a value-added export commodity. The Economic Fairways modelling reveals a strong alignment between prospective hydrogen hubs and current and future iron ore operations, enabling shared infrastructure development and first-mover advantages. By employing a site-based system optimization that integrates both wind and solar power sources, the cost of producing green steel could decrease significantly to around AU$900 per tonne by 2030 and AU$750 per tonne by 2050. Moreover, replacing 1% of global steel production would require 35 GW of well-optimized wind and solar photovoltaics, 11 GW of hydrogen electrolysers, and 1000 square kilometres of land. Sensitivity analysis further indicates that iron ore prices would exert a long-term influence on green steel prices. Overall, this study highlights the opportunities and challenges facing the Australian iron ore industry in contributing to the decarbonization of the global steel sector, underscoring the crucial role of government support in driving the growth and development of the green steel industry.

Ibovespa’s response to the behavior of oil and ore prices during the international crisis caused by COVID-19

The systemic risk caused by COVID-19 affected all sectors of the economy, thus showing the vulnerability of some sectors in comparison to others. In this context, the supply shock experienced by the iron ore sector has drawn attention and resulted in a price increase. Linked to this, and in a negative way, oil prices fell due, among other factors, to the price war between producing countries.In this sense, this study analyses the volatility of the Brazilian stock market indicator in relation to the prices of the aforementioned products and the price of the dollar. The results show the importance of the price formation in these markets for the variation of the indicator. The appreciation of Brent oil and iron ore prices on the Dalian Commodity Exchange (DCE), in China, caused the Ibovespa indicator to move in the same direction. In addition, in statistical terms, the study highlights the great importance of the exchange rate as a determinant in the variation of the indicator and, consequently, affecting the intention to invest.

Dynamic volatility contagion across the Baltic dry index, iron ore price and crude oil price under the COVID-19: A copula-VAR-BEKK-GARCH-X approach

The international dry bulk shipping market is closely related to the commodity and crude oil markets. At the same time, the Baltic Dry Index (BDI) is usually considered as the main indicator of economic activities. In order to clarify the correlations between the three markets, this paper employs the Copula-VAR-BEKK-GARCH-X model to explore the dynamic dependence and volatility spillovers between the Baltic Dry Index, iron ore price and Brent crude oil price. In addition, the influence of exogenous variables (BDI/Iron ore/Brent) on market volatility and co-volatility is further discussed. The empirical results indicate that: first, the dependence between BDI, iron ore price and Brent crude oil price is time-varying and time-lag, especially when suffering from major crises. Second, dynamic dependencies and volatility spillovers between BDI, iron ore price and Brent crude oil price have significantly strengthened during COVID-19, manifesting that the impact of market turmoil has reinforced the linkages between markets. Third, this paper reaffirms the role of BDI as an intra and inter market indicator, while finding indication that iron ore is beginning to emerge its predictive indicator post-COVID-19. The results are not only conducive to the investment portfolio selection of individual investors and institutional investors, but also beneficial to formulating trade policies or shipping strategies by countries or shipping organizations.

Econometric model of iron ore through principal component analysis and multiple linear regression.

Price of iron ore is affected by instabilities of microeconomic balance between supply and demand. Periods of equilibrium adjustment result in huge swings, growth or global recession. They also impact the viability of mineral enterprises and generate consequences to important global economic scenarios. This research aims to evaluate the market variables capable of influencing the price of iron ore through multivariate statistical techniques. Principal component analysis and multiple linear regression, booth multivariate statistical techniques were used. The studied variables were rate export of iron ore and concentrates from Brazil, steel production from China, steel production from Japan, production from Europe, steel production from the United States, steel production from India, steel price, coal price, China's Construction Gross Domestic Production, United States construction index, oil price and global oil production. First three components explained 89.12% of the variability of the data matrix. Multiple linear regression highlighted the significance of five variables. They are export iron ore from Brazil, steel production from China, price of coal, steel production from India and price of steel.

Performance of different models in iron ore price prediction during the time of commodity price spike

Future prediction of commodity price based on available data is very important for mining investors and operators. Commodity prices cointegrate and show Granger causality to and from one another. This research reviewed five different estimation techniques which are Bivariate Non-Linear Regression (BNLR), Multiple Linear Regression (MLR), Multiple Non-Linear Regression (MNLR) as well as logsig and tansig model of Levenberg-Marquardt Artificial Neural Network modelling to simulate the future iron ore price based on 12 other monthly commodity prices and indices including LNG, aluminium, nickel, silver, Australian coal, zinc, gold, oil, tin, copper, lead, and Commodity Price Index (Metals).Six different models were tested in the paper to forecast the iron ore prices from 1 to 6 months over 10 months period. Linear model (purelin) using Levenberg-Marquardt technique was able to exhibit the best forecast result with average accuracy of 5.92% for 1 month ahead, 9.48% for 2 months, 11.21% for 3 months, etc. It is important to highlight that high accuracy is achieved (accuracy under 5% between forecasts and actuals in 40–50% cases) by purelin model for up to 2 months forecast for the period between July 2020 and April 2021. This indicates that prediction of iron ore price for the coming month is possible for up to 2 months period using the purelin model. It can be noted that the period tested was unstable for iron ore prices where rapid surge in iron ore price was observed. Same principle can be applied in the time of next commodity price cycle.

Save the giants: demand beyond production capacity of tantalum raw materials

Tantalum’s high melting point and ability to store and release electrical charges have attracted high-tech companies since its usage in capacitors began in the 1930s. During the COVID-19 pandemic lockdown, daily life relied on electronic equipment, resulting in a surge in demand for electronic and communication gadgets, which could necessitate many tantalum raw materials and an assured supply chain. Despite tantalum’s high demand from electronic manufacturers, 5G network systems and electric vehicles are currently added to the tantalum consumer list. The authors have examined three interconnected issues linked to tantalum supply interruption, which has resulted in a growing tantalum scarcity: (1) rising demand for tantalum ores and high-tech equipment while mining activities are in decline owing to the COVID-19 pandemic; (2) tantalum ore price volatility constrains the ore supply chain; (3) the challenge of pandemics that shrink mining activities and handicap supply chain. To address the issue of supply shortage in the long-term, the authors suggest that the concerned parties may adopt new norms of reliable, stable, and transparent supply channels instead of relying on an old uncertain and volatile supply system. The authors also suggest the transformation of central African artisanal and small-scale mining (mainly in Rwanda and the Democratic Republic of the Congo) into a modern mining system with a new supply channel that can resist existing and future disruptions.

Multi-step-ahead copper price forecasting using a two-phase architecture based on an improved LSTM with novel input strategy and error correction

Accurate copper price forecasting plays a vital role in many aspects of economics. However, the complicated fluctuations of copper price make it a challenging job. This study develops a novel architecture based on long short-term memory model optimized by genetic algorithm (GA-LSTM) and error correction strategy for multi-step-ahead copper price forecasting. The proposed architecture includes the following two sub-phases: (1) initial forecasting of copper price; (2) error correction. In the first phase, to improve the forecast accuracy, a number of recent copper price data and some selected influencing factors are combined to construct a hybrid input of GA-LSTM model. The hybrid input strategy simultaneously considers the evolution rule of historical price data and the causal relationship between the influencing factors and copper price data, which provides more information for GA-LSTM model. One real copper price data series with a time span of 30 years is used for validating the forecasting ability of the proposed architecture and a same length of iron ore price data series is also adopted for testifying the generalization ability and robustness of the proposed architecture. The experiment results illustrate that the proposed architecture outperforms the benchmark models considered in this study.

Marketing of iron ore products under COVID-19 pandemic conditions

Marketing of iron ore products under the COVID-19 pandemic conditions became a robustness test for the metallurgy sector and the whole global economy. Most of the countries imposed various restrictions on the frontier crossing and business activities, the industrial scheduling and freight flows were essentially revised, and the supply and demand for goods and raw materials was accordingly restructured. The raised social expenditures in many countries resulted in higher issues of banknotes by central banks. The commodity markets experienced alternating price behavior of basic materials. Staring from the second half of 2020, the deferred demand for metal grew gradually, which entailed the additional requirement of the raw material and the rising prices quotes. In 2021, including recovery of metallurgy industry, the iron ore prices on the world markets continued rising. In the conditions of periodically shut-off paths of supplies (mostly because of the pandemic) and under varying demand for mineral raw materials, Lebedinsky GOK maintained its production capacity utilization and remained present on all critical markets. To this effect, Lebedinsky GOK improved the range of products, flexibly changed sales and logistics depending on the market situation, closely cooperated with all production units within the Metalloinvest Group and preserved the yield of iron ore and hot briquetted iron (HBI). Within a short term, Lebedinsky GOK successfully tuned up production of premium quality pellets, which was internationally appreciated, as well as increased production of re-enriched concentrate and its shipment to Mikhailovsky GOK for manufacturing higher quality pellets in the framework of economic cooperation at the Metalloinvest Group of Companies. In the period of the pandemic, Lebedinsky GOK successfully maintained stable iron ore production and shipment at the effective healthcare and health protection.

Multiscale nonlinear Granger causality and time-varying effect analysis of the relationship between iron ore futures and spot prices

Using the ICEEMDAN approach, the Granger causality test, and the TVP-VAR model, this research examined Granger causality and its time-varying characteristics of four types of iron ore daily spot and futures prices from January 2014 to July 2021. There are two major conclusions. (1) There exist several one-way nonlinear Granger causations from spot markets to futures markets or futures markets to another futures market, and the widespread two-way nonlinear Granger causations. Platts iron ore prices are more sensitive to new market information than others. The iron ore futures prices on the Singapore Exchange are also more sensitive to those on the Dalian Commodity Exchange. (2) The effect of one price on another has a concentrated influence, a brief duration, and a fast decrease in intensity. The effect's intensity, which shows periodic patterns, varies over time and is unstable in positive and negative directions. Therefore, we suggest that (1) nonlinear models are more reasonable for predicting iron ore prices; (2) better private decision-making and public intervention should be based on the multi-time scales variation features of causations between different prices; (3) principal iron ore consuming countries, such as China, should speed up the construction of more mature futures and spot trading markets for increasing price influence.

Multifractal Analysis of the Impact of COVID-19 on NASDAQ, CIOPI, and WTI Crude Oil Market

In this paper, we explore the impact of COVID-19 on auto-correlations and cross-correlations among NASDAQ stock index of the USA, China iron ore price index (CIOPI), and West Texas Intermediate Crude Oil price (WTI). To find out the effect of COVID-19 on financial markets, we divide the investigated data series into two sub-periods, i.e., pre-COVID19 period and post-COVID19 period. First, multifractal detrended fluctuation analysis (MF-DFA) of those series shows a general trend of strong multifractality after COVID-19, indicating lower market efficiency after the pandemic shock. Second, multifractal detrended cross-correlation analysis (MF-DCCA) method is employed to examine cross-correlations among NASDAQ, CIOPI, and WTI. The three cross-correlations all increase in post-COVID19. The correlation between NASDAQ and CIOPI increases the most, becoming the strongest correlation among the three cross-correlations in post-COVID19. The surrogate procedure shows that the post-COVID19 cross-correlation multifractalities are mostly due to fat-tail distribution. Third, we use multi-scale multifractal analysis (MMA) to visualize the dynamic behaviors of correlations among the series. The Hurst surfaces of the three cross-correlations have more fluctuation, both at small and large scale in post-COVID19 than that of pre-COVID19. Particularly, the Hurst surface of cross-correlation between NASDAQ and CIOPI exhibits stronger multifractality during the outbreak of COVID-19 than that in both pre-COVID19 and post-COVID19. The above investigations provide helpful insights of relevant market trends.

Developed multiple-layer perceptron neural network based on developed search and rescue optimizer to predict iron ore price volatility: A case study

In economic investment, the role of forecasting is very important because in an economic project, the investor must carefully examine the dimensions of the work such that one of the most important and perhaps the main factor of a future investor and an economic enterprise is the work done by Costs and revenues are determined. Due to the fact that the volatility of iron ore price is affected by various factors, so it is not possible to determine a simple and general function to predict its price. There are several methods for predicting price, but the most appropriate of these is a method that examines variables in a nonlinear and dynamic manner that is closer to reality. Therefore, in this research, an improved and optimized neural network is proposed to facilitate this task. The idea is to employ a developed version of Search and Rescue optimization algorithm to enhance the training ability of the neural network to present an efficient forecasting tool for iron ore price volatilities. Different variables are used for the method verification and its results are compared with basic neural network, particle swarm optimization-based, Intelligent Integrated Optimizer, Genetic Neural Network to show its superiority. Simulation results demonstrate that by the proposed method has a satisfying and better fitting with the data compared with the other methods.

Decarbonizing primary steel production : Techno-economic assessment of a hydrogen based green steel production plant in Norway

High electricity cost is the biggest challenge faced by the steel industry in transitioning to hydrogen based steelmaking. A steel plant in Norway could have access to cheap, emission free electricity, high-quality iron ore, skilled manpower, and the European market. An open-source model for conducting techno-economic assessment of a hydrogen based steel manufacturing plant, operating in Norway has been developed in this work. Levelized cost of production (LCOP) for two plant configurations; one procuring electricity at a fixed price, and the other procuring electricity from the day-ahead electricity markets, with different electrolyzer capacity were analyzed. LCOP varied from $622/tls to $722/tls for the different plant configurations. Procuring electricity from the day-ahead electricity markets could reduce the LCOP by 15%. Increasing the electrolyzer capacity reduced the operational costs, but increased the capital investments, reducing the overall advantage. Sensitivity analysis revealed that electricity price and iron ore price are the major contributors to uncertainty for configurations with fixed electricity prices. For configurations with higher electrolyzer capacity, changes in the iron ore price and parameters related to capital investment were found to affect the LCOP significantly.

Prediction of Spot Price of Iron Ore Based on PSR-WA-LSSVM Combined Model

Aiming at the problems that the existing single time series models are not accurate and robust enough when it comes to forecasting the iron ore prices and the parameters of the traditional LSSVM model are difficult to determine, we propose a combined model based on Phase Space Reconstruction (PSR), wavelet transform and LSSVM (PSR-WA-LSSVM) to tackle these issues. ARIMA model, LSTM model, PSR-LSSVM model, and PSR-WA-LSSVM models were used for contrast simulation to forecast the spot price data of 61.5%PB powder from January 30, 2019, to February 1, 2021, in Ningbo Zhoushan port. The experimental results show that the PSR-WA-LSSVM combination model achieves better prediction results. At the same time, the model has a good performance in the multistep prediction of the iron ore price.

Improved Convolutional Neural Network based on Modified Search and Rescue Optimizer for Predicting Iron Ore Price Volatility

Improved Convolutional Neural Network based on Modified Search and Rescue Optimizer for Predicting Iron Ore Price Volatility